{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "from glob import glob\n",
    "import shutil\n",
    "import sys\n",
    "import json\n",
    "import time\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt \n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "# 这两行代码解决 plt 中文显示的问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "f0818dd2-f9c9-48f4-890b-eb466f645973.png": {
     "image/png": 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uU6TWgvO/fE2NfH6CsdYKSmoEC3Y/FVlxxHo4YH62gjFhcq8VARkdry6S6GvJXr/7NEx/D/EgvG8DvLvkh6+dGy7HH1E9MU51RhSnwr3x9PT87uIVforI2x8YX1rfPoHA6yU3wQ1v91AC00oZn5tmamqSycnPZWygk8arLjiYeBKY/IR3o5/3aY6bmWN+Q90qFnoqKEu0Zo9pHLdKu7RMhASwlCyO1FN7Jwjb/XpYWeijb2qDlW8SGY3zLAjkg1ZMN26Uj1aQmxqKm74eOnZx3G+e/nFru1qKZKaVZ5Gm2OvtYPv27d8sOw4FEpvxjmaJHNncJGPjAgkgGGGUTHW852XkAXaZntCQLxsng2okQ2W8uxmCyba9mMa8oLBtEolayUxfHYXnvQj0/obu5eaBl99hUgrH6Jn+2APVcyxNveOapwEWe7XrX9uN/PA5cYPGojhcDXTY+zX9a9te9ui6EV80TNe3mHbFEvKqcxx1tMc96hbZ3T+ijGx8rl/dIm+mOP0o/mY2WF5oZGDmByZbqJhpK6bwymE8vqXfftd+TyLPP+Rp8wZB+5Vb+DcRD2oJ460F3PXaio5HGlkVfRtJBdUo9c/OcczRlL2Rb2gdX9xo9f1iSxVM1T4hO8aBLbox3GsZZ/SLtyljoqOYe16/scfrCtlV/d9FPKgX6ym+fRRfUyssL9TTPSVF8A2bb31D/lkHdm/fzo6vvI879jvgFJFJpZy1JLtyEvl4NvF2hhjt2Ma2bX+mbGevsTOOl5vp1bRvNWAqkLeQm+SLn3MAPlc+MKeZTn8+Rjnbx8TrSJwP6uIUfYObb1vp7Oqia1XpG55gUnCV+NHfnyEeFIM0vUrllK0eOwwDOZPTQtuEdIPsVXQ/Jzs+AEvzI6R1KJjUEA8j1Ockc8zeCJ3jpbSNLn5hHPx0Q0rUqhmaM4IItLHHNSaDl0My1OpZ6h+fJzUu4rvmQjGpz8j5MLyuD6uRDVfx4U4oBn/sxiW5iOLOOQTfGfl0NwPZfjgcMsX77FNyu1Yb8BUMFV4ixc+CgxZHSG2WaTwMP7V45V81TmPuba4dDyU4OPhfLGFExV0gvUbOpDZr0MrF/roFLcSDGqRj9NVVUlPTTPuMMJCoUI095UqwNc6ux0hYIR7kzHQU8CIlCJeD2/h9+z4Omthi4+CMi4vLx+KErbkhxlZhxN4o0lj/196OGqV0kpn2p6R4GmLjeITYOxX0SBZQSyp5knSMY0GBBAQEaC3+3u54mO1h5x5/4p5U0bLChgvEQxM5Z73xtrTDLvYur/PzeZOfT35+Hlmpx4l01OMfNtfIryqmuPA5WXfvcOdOBhkZNzjurIOBmQMOoYncuiNsv8Odu48pqO2lf/5bL6WcjqfxnPJywDDgDvWKdYJoLQB/bk1DPGQQsf3vmEY85nmdYKX9zp9yhJGW+5zS34GJhTNOXoGEhIQQEhJMSIgvjsb70TMwx9zR5+P2EHxsDTDUM8LQygU/zbHC8SEEe9phZ2aNiVM8D0aUTH1xUNDStn+ZeFAjHSoj/+oRnPVNMY9+RnHP9OdJqFoKshayIyxx9z7BiYfNK94BitFc0qM98HCPJq5wai0rrKWJy5sEV455BkpukxrmgJXeAQ6aWmPj4ouPXwCBgYHrih8eDpZYGJlj7naCW61S7cLk0/UUY4y1POSk7m/YxT7hWd3UOqEmXL6B8gfH8NYzwvZKGy1jwuD4HT/ZFENNxby5l0ryiUjCA/3w9wskJOIE8WlZPC3rYVKu/ugWtq4+xTSDDQW8vJXEqcgQgvz8CQqL4eSFezwuaWVwSYtyrhao9nE6Sh5z/9IpYg8HExgQRPCRE5y5/pyChsGPYS/rrqVtVa1ANttH+7NTRNhb4BqWSGL6PXKuBGN+wBy/s0/Jax7bqOhoq0slZXG8jcbXNzjnbYylpRuB556Q2zKNXKAflN08PWqFp+9xjmd3IFdLQF7F/aPuRBy/wc2yKVRCv1I08uCwBW7eJ4h73LX2SuoFhqqzue2vxzbbFB7VDDI+3kFH6SOunEsiKeljSYwjOsgF6z2/8ouuJ4FRJzmTuGq/cNy5a2QVNdKqkcPCZdQoJqt5eTGSwwFRHH/axExpHIEWnoSdv0NmYTYZ4fZYhN4nt2mMGYWK+Y5iihOs2Lw/iusV3QyuyMi1zV67pmCw8AqpIY7ouN+k4hPx0J3H6yQv9m0xwiPhCinXb3P7tvZyM+UUiT4mbPrFnTOFrbQLRoU1Pym9+Ze5GOTAfs/bVE5JWVjTmQUPlAlKL3rj5+KPT3IB/Ypvh62pZaNMdzzlQlggx68VUtw2/eV3RL6AsvsJtxKOcir1Kc8bFzYoOxqqUiFhob+a909vcTXxONHhwQT4+eIbEExY1CkSUjN5VFRP57TiO1zsVajk80x2lFL0+CZXkk4QeySEQH8//PyDCAmPIvpUEinXs3haVEfHpPyjlVfFXO8HStOCcYnNpritiXcPThHjZofZmXJGPxIPaukAfTXPuHHiGCeOHePYsRiCXc0x2beNTToO+EdEEx0rbD/O2bR7PG5Y0uSp+fxoBLfQMfryEjjqYI5DwFlSCrqZmJ+gIeskiTEhBAUFrS2BbjiY6XFo70EO2fjiF/B5f3BMIifv1TG5nhxQLKHozSLJ0xLngDMcv/2awkt++Hm64rqiP7jg7GSP7aEtbP51GzsOGGPp/Em3WP53Cz/HuUe1TGvcitUad+mxwU5qC+5z3Xcv2+3OcuNlBc3dPfT09NM3MPExdEjF4qDg/XSCQEsz7I9m8CA3h0dXjhHp6oBF0GWetEwzLlnTKT/DtG5JLZthrK2A55cjcDmkh01kFrktY98dGrJcnUKDfVPOTe5evUDK+TOcPOKNw+6f2aTjhE/ESRJTUkhZVS5ceUJuVSd9w220ZZ0gOOImz+v7GVcpmWovIeeIDjuMjnOjpEMr+aKWTjFS94L7h43Zf8iD2PuV1I5LmR1opCQtlMjQL+tfAb4uuFgbsH+XAUFZ/TSNfcRKPcviRAEXbHZh5RJJ1MVsjf6Vn/+G/PxcbkQ74mTvgWPsAzp6y8l5dJ97Gj3rNmmJsYSa/MZW8xCizlwmXbP9HvcevqK8f4GZDXJk3YMQiIfKRCLtbXGNvMmjv5h4eHs7El8Ta8xTGn6YeJgVPN5uHyN4g+4QSGCAPwFe1pgc2MsBXVPMXbXpGKt1jhBOXnnGq7YfUb7+TcSDYoieiltE627BKq6AwrbZjbrLbDX5V8PxNLXHJb2HAa0W3dXPUvA4Wg59HBnso7PmBS8uBWG4zYyo++UUNw8wMjHL3Ib+sEw8ZHr+ym7PK2RXfg/xoGah6QHpMZ6YW4WTWDXDhEQIX1IjHW2l9fVNEs+c4cyGkkDCcV8c9Hexc5899idfInS3NcZsxQSy0btE6+/CzMofv+OXuHLlyo+XtHii/W2x1DPFKKmersl1nsEqBbK2uyT7O+Pid5bUiinNXGBltqKaZaL9Nfd897Dvt1/YvEcPPQs77O3tVxUnguPTuV+3AdTVD0b78p8hHlSCR0AtZQ8SOWKpi6l9KCfvlVI9tPYeBeLh0ekALMyOcHmFeBim/kUysbYG7D1WSutXiQc1ioURJsovc8xeDwu3E8Rn1zMkUaBWTVF5O4ZTwa6r5q9rx5rP81oXvE/d4f77/g39XCUdZ6TuKdc992JgGcbpR9U0z6hQy+eQDhVyzd8UB7cYTj6oYUiji6lRTVfxPNEHTxtXnE6/onVBrT10WyDvcq6RGuWHj4/PjxdPB+zMjDC28OJogZThb85ltT/qP7tVO/Eg6aQs8xwpZy5y6XUP82oVitXEQ9EcKukEvdU53E8Mwt/ZAQvXKBJvZZJ+N5PMzOVy9/ZVbp71w0ZnM7uMw4jOqGTw0xiunmO4tYqqsioq2sdRKiZoeniUcFcv/E7eo3BoCbVyhI7qUkoLCykoKNBa8h5ncDfsAHt2eBKTVU7jisz9RDz44Ovgideld3SssHodlD+5zDlvA/5hc53C9n76Bzpoq6ukoryU8vLXXPY9hI1rKCEpTyktr6CiooLKhi4GJhe0d4g1T+I/hXgYYqjxFhFb/wt9t3jibzz9SMzkkv/6Difs92Fs6Y1P3B3yNITNa9IjLbEytcU6KInM1wKJs1xeXY0m2tmKPcZHuT2kZOLTc1ALroTDdFZXUF1RQXl5+cby/h43Eryw2r6Xg0ce8jD3HWXajiuv4ENjB52rJttq2QT9lVncirBlzz5nYu9VUTu8zHgKCpWk5TrR1vsxsQ8l7MIT8gsLKRTK61skBtpgZ2aHw8mHvC74uL2wiHeVjTQOCpbtVQ9VucTSeCs1L69yLtgSkz272KPvhHdCOg+fPefZ8+c8XynPeP48ixvxgXiaHULXwBH3+MeUjSm0x9fLxxlqr6MqP4vsq2FY/LYZ68OXuZSVz9vi95RWNtAxrUSiVLHU+YKnCa4c2mmIc/JLHuW+5d27d2tLSTl1HSOMCG7rgsVe2kfdi2tcig7E19UZJxcPPL298fbywM3RFnt7Z5xDzpL6upO+aekqQbpMtPSX3ufWCX887a2xtHfBzcMDdxcH7OxdcA89y4Vn9QxKVJ8HW7VgeevXWFnPBrvgbG+HjaMr7u5uuDvbYm3rTsCJm9x718mE4hthLmoZi2Mt1D9P4ZijHlbux0jMrqJpaITJllekH7bA1s6HkKRHvGoYYf57wnzUMk37OotucT7IDhfv45zI/MCQXI56A/GwBLL3XPc1wTMomZTCCZTfIB7UCx3UPk0i1NgQ25QqagYXkI3XU/fiIlF+vvj5+uLr64uPqzVW+ns1Eyoda1ecPX0024V9K8U/hpRHpVRrYjCWXfYWp3uoyblN+qWLpDwppyc3Gm8TZwJOX+XGq6dkxscS/6iamr5pFmSLy5OKgD38usMa79gznLtylatXV5X0VxQ3DjCpcW+XsjA9weT4CA1Pz5HoZ8tep4u87hqkb2qBuZYc8s75o7vXgzMl3TT0jzE2pr0MNxRQnuLGlt88ObtCPAh5d2YZbiqj6v1Lbsf54W1hxH6vCzx6XUhh8TtKazvoGFlEpZKgmn3PrRBzrM1dcT2ewev1ff1dCSWlFVS0jmnc+YU3Vr3Uw0hVEm5bNqEfmE5m+egXyVhhsiivTSLcUgcTr3Ocf7uepFAinemnu/QB108F4edkjbWFJVY29tg7OuLk5IiDnQ3WNg44+UQQm/qY/LZJpr4U/qNcYHaogcqnV0g+6ouXkx22NrbY2Tvi6OiEo4MD9nY2WFmYY2Hljtvha7zqXfjoyaVkoqWAJxGH2O5xm9z6Bt5rIx4kvXRXPCAlLJgwjVUkEA8bA/R3b+GX3Ra4CSSpZnsoMYk3uFuz+Nk9W61AMt1DR+ENEr0MsXQ8zLH0d9SNy1AqlphoK6fqfdGyDP0kSzXy9BqJIXZYHTDF6ng2z3PzV44pel9FWcuYJmb1s0hVolgcoeN+IF7WrgSezeZ5VQPVd09wMiaKyMjIjyWCw6H+OB74hS27DTC29+Hwyr7lY46euc2tvBZm1WrBrkT32ztkno8i1NcZm32/8Ms+W1x8Q4k8epSj0QmcSnpM+dQMfV3lFNw6SbSvKzZ+ydwp62dwdlZjLc1LO4qXuSXux2+TVfY9k6TlsUItn2a08SUPY63R03Un+k4Z1cMbLXerRpZ1i0I8uYz58QGG+nvprH1NTtoRHPbuwTwmi8cljbTUlfDu5SOupedS0dJBR98o4zMLTPVWUXnWkt2Horj8tpV+5ZeJByGcY7ajjLJ3VdR0DDM+3Ud/0XmCjQ3wOpPN06YpFucnGKx7S8nbQgq/oH8VPL/GtRMuHPptN953e2kY/Wj2WEU82PknkZxdvcqy2s7rFF/8XD1xPPWC3sUZBtrrqP9QQUX5O149uMQpq83oeF8gLesNZYL+VV1HTesg00sK7eT4ahT/Y4gHNTIhj05TKUWr36VPy/l5vLkegruRAWbOEULHLhUAACAASURBVJy49/md0ugwn45b+S+ivL6T7tU52DQ5KLpoqfqC7lX+nvKSNGKdDTA1dtd4HJd+QfcqL6+gtmOYkRXCQA2yUfqaP1D55i53zwdi9vt2bGLSufG4QKO7lNe00D0r5GFSacK2MqId0D9gjcelPF7kF6/TW0p4V1JOQ/cE4xqLuwKlbITqrPNcjo8hOjIAf1cTdv/zDw45BuF3+DSp93PIrWmk7ME10q9/GtMukxwXgc+hn/jtkBsB0WdIXRnvrnM14w0VXWN8cs7RaCCqaZofnuC4twc2R+5RNafmE9+oks4zP9ZHR3u7prS3t7NcWmlrriL/SgjepnoYuxwjIbdHYwD5NM3RdMsV4mE3TqHXuJLfzeDg4ErR9P2qcorLhFDuQQZW7evv6aT9w3vKqhtpbnvH82uRBJhqIx4EMnuatswQglz9CDz7jMrJVa1QK5gfqOL97UgcdmzG1OMIobHxJCQkkJAQT3z8SSKcD3Jw90Esw9J4sJ68Ejyx5oboqPqaHn+XKyfcsdx1EL2obJ68Kdmo62v6VgUfmjrpGl8nF5WLLIw0UPUwgSN2Jtj7nSUlR/AimmCk6ilPszJJTz5MqKMxe/dY4JWUTtqtDDLSL5FwxBXb/TvZbHuCxNQbpN99wevyNvo/zUM0D0KFdLqXjrfpXAo0wsjMh4ir+ZT2zms8W9TqJQZrCynOfcqTJ0++WV4U19PYP6vFqKFENttLb+5pwpw8ibmax9s+IXeKErVyhpaHp4g/nkxyVhV9MsG4Mkf3q3hiPJ1wDkrmZsUoAu3zeaxcJdzUC4x11FJdlMurV69+vGSdIyHYDuODDoS+ljK4waV61bX+wkXtxIN8kOrMY0R7umMXkUnZuIz54U8eDzGczOlluOoB16Jdcba2xtrtMDGXHpFX28vQnAyZSo1KNs9MbyXlt4NxNjTG6Wg696vGPro0q0HZR+ndBOJj4jn5qA0hmkUxUkzO7StcTc+lZGBNj/kCBGqkY210pJqhv8+bWK3EgzeeJqaYBiRy7cYNrmtCQK6RHBuIr6UuPwnEQ9coXfX5vLl7gXPJ50hJOYW/2W4MBS+A0NOcP3+e8+fOkfKokoa+mVVK7HISyd72JhoaGlaVGl5eDCXY1pT9Tmd4XNtAVf3q/cvLTT3jTC38WEzgBiD+lMfDJ+Lh71hEPyOn4WN+CGFipezgYZgxzr5xxD/r1DwfIYSk9ZYP/u7B+Ke80TC7n9ojqbnL3QhX9q4nHlRTTPUXcScyhKPh4YQf1hLOcdgLV2tddm/6jc2mPngHhmlxdQonLDSU6KTbZJRPrFE+FLPddBddIFRvM0ZeKVwv6GRUIkc60UbzVScs92xjt54V1l7L3hnLXh2+OJns5+Devey18CIo+NO+UI4m3eL2+1HkQsys4IwrmWK8s4ziB0lE2etjbmmHpYU1zh7BhJx/SnHrMGML8uWJt0qY1A7T35jPwzMBBLh543cig6dN05r9WoXJXDWFGYkc83LC1c6IXZv3YOzghWdAIEEBIYRFp3CvRcrE0jy9eRdIdj/Ib5sPYuYZhG/Qp3YL/8GEBAfg7xfGmcx3vBdiW1USWGrgebw/wW7ueIYncP72E3IKiikqyuNFZgpngu0x2bePXe43eNY4ytRHi7hauYRyopyso3Y4GJth6hnL2fQnvHzzhpfZN7hw1AMXC3P0nBO43zyrsRAI/UEtHWe88QkXnXajb2CHy+EkUu8/JzfvJTkPUjntb4G5iSMu0XfI65WseZaf+pPmX7nIzFADFY9TSPA0RsfQg6MZZVQPLGhyLyiXJpmovMG5QCusbX3wPX2fvIZ+xhaV35FwUmAo5uguvM7182lcelBBj0xIdLbe4+FHiQcl8x0veXouEHPjIC5WzTA4Okh/XQF5D9NJuyZ4VAkeVOlcPu6Lj4Uum/e7cjTtFlfTP3pWffKw0nhZPeFNdSc9AjOtIZE6+ZD3hKzLF7h0PpmkS6ncOOmIyV59zN3CiUpO5eKZBM5du8XtvDqa+3toL0nnrPmmZQuHkRnmFuaYGOhyYMfv/LZDF327E6S+rKVHIUE6303180we3rlFylEvPM0OslnPl5Np17meU0VN4T1yzvlyYJcj0U/KKajeKNc+ycLagixyTtnzx68eq4gHGUsznRSnhhIT6o69mT4G+w9y0MZHMxkO9PMl7GwmmaXDyKVTzH44R4TVfvbvNcDI0Y/gVV5WQn8PDgokMOQIh29W0jE8r+k2/07iQbk4RE/ZPa6EmLJvuw4GNj4EHUsmNT2Lx89e8OJZNvdvpXD2qA/ulobs32+BR9ILCtsnmFlj9hJeDBnzgx8ovXeaI9Y67N5vjKVbGBHxqVy983C5vqcPeXDnOpcFr4qwCAIjrpM/tMi05p2UMlz/knSfHWy1P03a/fvcjPfDy8KQg0E3yX1bSkPvFJOzQh6OJipe5/E6L4+8vBxuxwfiZ3GQrZaxXM9+wYtcYftrispqaRiWf0zmpUQy1U1L/k0uBZpw8JAdARdyKWoXvIG+8ZN9IPdSED7mbvjeG9LkN9Aq6z5Vo5hjvr+Y2z46mNjHcia7lsHFReZGuulub6GluYnGxkYaG2uoLn3OReetGNhHEHP1FTWa7cK+Jppb2mjvGWZ4UnDbFq4oo+VZMueDTDA+sINN/9jCPlMbLO0ccHBwwMHJB4+Aczwoz+fe5QiC7YwxsPAm8EoB5U1ttHd20ikk7s29xQUvI/R0zXGLuUZ6QTPdk0urxv5PN7LxX7kwyFjFVSJN92IVeJlrRb1Mf7cb4qr61EuM12bz4Lgr+3V9SCoapHdWwdJAOaX3E/H1T+Zxwyiji0ImGhWL3008CIkAZ+jOjiH2cALnH1XQKZcgm20jP/UUlzPzKWqd/PYzF7r09Ac+PIzE4o/d+GolHnZibuODT0zKR93rOjduXOW03zKx5nj6Fb2SeToKbpN5LYXk5LPEHfXH5cAf7LMP5/DxRM4nJ3M+LYNreR1MLa2Ke1ZKUcwN0tXaRNNq/au2ipoHR/A2NcbSO47zuXWrdLOP8qqxhabWQSaliu+6z1VPZe2ivJl/3eNhbVXr19TyRWbzjxFq54rXyfu8+XYCoPVVaPTs5qJMUo+EEBJ2mMMb9K9wjoQ7Y66zlR07DqBrF8DhDSG3hzlyOJzQ4FBOpRfxtl2YqAkPX4l6poyctFNEezriZGXIrq06mLr44R0geGyEEhF3nexOBfPSOTqfxnHC4QCbd+hj6R1MwIrO9VF3CQrAzzec5McfqB5WauSlQtJH0YUQIr3ssbOzWy62NljpbWfrHzpYhsaR+OgeZ41/Y+8+XQ4Zmq6Mb/t+/xs//b4HnUNGmm1mJgYY7tvGHzphJOc10P3J80+tQIivv3/MCx/vKI4+amNBrWRhuJX21k46h6ZZXDWHXwZZSFI9wXhLHreCzbCx9CAoJYeyMS0v+grxsAf36Ic8qFvtYq9G2l1CydMsrj4oo0vBqtwVKiRT/bS9SOVGdik1XdUUZ50i3FwL8aAQjBvlpAeZ4RaYyPmXrUwuzjHZWUdT9zADPY1UZZ/jpMM+fvt1F/aJr8ltGGZiZpaZqVEmesp5fNwBV5dwIq+9pWV9riXVBONd+aRHBBP5RT3eE2fLA+z6dTNbzXzxCw7/oh4fc/4u9yonN07a1TLUkj4qM45zMiaZC48qaR3qouVOGGE+LjhY6nFo91Z+/XU7+y3ssRbkur0VZnp72fX7Jv65yxhzK1scnCKIu5nPh8VPr4SCpYkujafrpXBbdHab4H76GW+ah5mVy5F+NVnppzq+/q8WdIWmasqK3/K2MJeil2nEeLgQGJnI+cyXFL19y9u3ReTfTSQxPpH4i5k8fVPA28JsUnz0MdI1wzLwLLdyhePeaoi7mk8GxA2XVqEUcgHNLmoSd391rF11rmq8mLxLwdgdcuJwgZSh/zkeD8utlPbk8uSML9ZGboS+GKav8xGXNaEWkUTdKeFNtAFmhla4xtzkeuZdnl05gr1HEvfLuugen2a8q5x3GTG47tvMAesTXH7dQv9KbLHgjdBObpIPPg5euKXVfGR4hMRIy58bWYXVVxa/j3hw0dnO9kNWODoKViWhOGBtcgjdfTofiYdhjft6tM1WNm3VwcDQEAN9PfT09NHXN0BPdz97fv0vfne7RlZZ/2fyRC2n7/UlUo8F4LUm5tUDB2MddLZv5tedhth5eOK+st8DDw933Fxd8U56SXHL+HcpM18E4X868aAcZKjxJmF//I2d2/ejo2eMqanpunKIg3u38ttP/+TnnQboGZqs2y8cr8vebX+w1zyQkKx+Dbn1+WUThEortRessdS1wj/lJQW9o4y1POO8+S/s3LSNPcY2OPj64+//sfjaY3ZwBzu37UHHygs/v0/7Ajgcf43rxcMfiQc1SwPvyb8aiuOBnWzV9SD67nsKC7LJTvLDVt8Uq4h0nlT10Dc5y8xYFx3vM7nob4SxgQ1eCY940Tz91WesmnhJepQDplt3oyNYdl2XY2pd7U0xOajDngNuHC9dpH+skbwLgXjp7mC7rjnW1jbY2Kwq1hZYm+5j+687sIu5x0NN3JEwYi7RW5qrYc2bxte50AmhI5UPuOa2m5/+aUlMTjONH2NolQujzBRE4WWgg5F3MpeKBlfFSqpZ6s7jRZI7Brv0MD8vTGCWFXPZcA0Nd/zY//NmzGMe87xhtRKrYr4hnQuB5hiZ+RJwt1Pj9rxmXNck0FtifqSGopvRhAmklK4zARn1NI8vIhf2r2SQljNacZ2UIHP09xth5JvCg5oh+qeFDOjaMr2ve5PUS0glUpY05g1toRY/SDyoZmh5lkiCnzPGhx/TvCBloj6D63GxhJ/MJL9viUWJFKl0hqYnZzntZcMB3wyqJ+aZlsi++PUHTatVCzD7klR/Wyx2bmPr5t/49R//xX/9/e/87f/+jf/66Wd+/m0zW7YJseE72B94k/tFBVTnnCFw3w4MQ9JIuX6PrKz7XEs8SojZVn63jCbhWh6VHSPMKKeZGXjNJQcdjHZvZfNvv/DLP/7O3376hd83b2G7y3muXbvIkyQ3dm3aib6DJ65eH700BLc/b0/cXV1x9/DCy9sHHzcHXEx0+X2zD+eK2+gQZq9qCfMjH7jntQW9Awc5ZGqLqyZ+2QU3VxsMdv+BjkMsxx61sjDVRfU5C6z1D7BX1xiL1X1ds2yBqYEOe7bvYHvYC6o6pzQw/fuIBwWzzc94fMqWXZu2stPuNNffdtKnJYGNYrqdhufniDDZzq+/mxGWXk7lyFpSWS3t50NWHFHmO/h1uwkOcc940zbJjNZZvQKZdIHJsWmkwpgo3Jl6ht6K+5yz+Cd/bN/HvoO66OzawuZNv/DPrTro6ZsQfPU9RR2LyGRCH/tU5ml+nky8lzm7ve/wYXSW2ZV9MmQygXgQLO0L9Jfc5lqQETt3GWEbl0dJt+A1I0GyMMfMvBS55jgFcpkMqUz+Oazkh4gHFYrpNnrzYnHY/iuH/G+Q8X5EIyPVgrvwRA9drY3UC0nYassof3uHk+abMXQ5xslbb6hZSc5WT2vfJJNrPtMiZKYfY+TDIx6d9UdvlxsJuZUU17XQ2tZBZ2crbR/yeHDMHptDBzloYIuT/2GOxsVxKi6OuE/l1ElOxUQQ5W+F0UE9zLwTOJ/XqQnVWyOr1omT5VUFsrkeik8YYanviN/5HMo/m1e1nrFxoxrlQhvvbscQbGWJwZFn1E0uaWLHFWPVVGZGYrdbB5uEfIo7ZpAKFuUfIB6ESVNLmgOO5r6EpOVr8lAJbRCSev7IZzW/h3gw2HeQgyY2a/Qvc90d7NJzwfH0S7rnxig4qY+17k627TmIgYEB+nqHOKSnr1k+uGc7O3SM2Rv1hp6pz+SPenGEufq7nD0ciP+KfuWJp4cbHjYH2L15M1v36GPs6LEmH4mgf7l7BOETdouCkblVlu+NT+GbW/4y4kEIQZ6h/YY7brYBhKS+oUmrnPhGC+X1FN08jPMfm9i0yxBD4/W6l7B+gF1//MKmTVvYss9Qi+5lhImxDtt+/plDQbe4XfZxwqiWoxp7zCVfc4x26HDQwnUlF5urvTGGOjocNAvhTLWEick6npxyw0V3F7v0LNbqLYIstzLDyngvW37ejvPZV7zsFFgBIdRCysxwD72dbbS1CaWV1qYPFKd64mrlgV/8VR4Iifz0fmafXRSxyTd5kHWPmxfjOGz8M5uNA4g4c4XMrAfcvXqOc5572b4vmLMv6zSTfM01FPOM5h0nwsUd7+OZvBqUoZJN0ZIRztEjZziTVU3PauzVKpTSaUZb3vDsmCmHtu7E7MhtMqvHtZNYq4mHo/e4UznD0pJUY3ARrODjRRdICfHG1O8G5RIZ0zLh6xjLJOpk13uyfTez3+0i994VUKCVeFCjnOtlvOgY7vrWhFzMo7BvlumeSl7FOhCYdJ+r52OJ8zFj3/a9GBruYq9zMjcL2xhekiKd7qK/8BQ+hvq4xGaSVbve60+wvvXTU5VG0G//l+07DrBfT5uOfogDu7fwm6CH7DJA30hbX9Nlz5bf2SfkRXs8uCzzN3RhNUiGNblqxmfkyBfGGal4RHbmbW4khRHsaMyePeZ4JF7n4lUhl2AKp8JcsNERPB6OcSYljRu3nvCqtIW+T+quYpy2l8kkuOuze7cJJhGPKe6dYWZxnPGhXlrbx5gXwjg/Tyo2tOqrG4Q+0X2Pi0EW7P91E7/+sYUtW763bOaP33/n9z/+4I/Nwjmb2bL5V375eSt2px7zRPg6wbqfWjHH3HgH7wuq6Bmd+fwFlnXHrV9Vjb0l92IwtnouRBX/DyQe1PIxWvJSSXA2ZJtrOkVVt0gMWs7xcDpvlMXuciobeugZnmNurJ32/EtEmu1CzzqEwzFHiAqyxVBHlz0OCaS/76ZvdpWSIlgmtBIP62H61vr3EA8++Dn54nejlrGpKSY12YHHaH5zi1Q/w+VQiy6BeDjO8UB3rONyGZoYo7flA3XC5xa7+uisK+Ku+88c8r++gXjoeZnIuSPuOK6Jk7LDTHcnOzf/zi/bD2Fhb4/dyn4brC0MObD5Z3Z43eRRxaB2YfWtW/+0/z+GePgJy4gsst73ML4qcZgmOdhICbnC4Lj7EIbx5ZS3Dmk55jXp0c642GgjHgRj8CzyoZekRR0l+fZr8stKqLnty4F//o1//GRF0JWXFI+tysQ+9oaMGBc87AI5/KiX8anP+2bmhE96fv5SgnykjHcPkgkPSSKzanlSK5UuMtP3gfL7J/E33o2emSeBR2M4FuWLo8EBdpuHcfZxFXUDc0i+8VmhZeLBBRe7cI6/GmF8cvkLLhPCpOeMH2YHXTleukBz4WUSfeywcY3l/PthxibWJVkb6WK85jxhpsZ4H7vPoxWBpUYpk2gmGMLEYe1PxVJXMaWJ1vzy0x58b32gfEhg7ZUsjbdTFncIYx1bAlJzeT+59hNzavkQza8vEWOyk1+tU3nRPMKMSsF4wysehenyy2/OxAkJDuc/YylcWy3tpDAtFF9Tc/RDsmhaVKzKsi94S80y2/qUq+G2WOjs5YBVMJH3auibk2kUbOncBBPDA/SPziJVqzWu+4PVj7hz3BmD7dvYesiDo7feUdE3qyE0197v+rXljPjLCff/PPGgmnjLw9NB+HkcJf7tGIuyCcpTvfF39sEjNoP3dQJpVUDBm3tcOOKMg6EJhqHXefLqNW8Kq2nsHf+KIqwC1QJTIwMMdDdS/uIKyc672GdwiC2CBcA8gPDLL3jX1E1Xdw+9I9NMdxVQdDUQ092G+D7op3FwgaWlKTpK7pPqvpe9h5/zTuMGr0l/hFw6Tk9NGZWlxTy9FEmEkwk7LGK4kVtIQV03PZXZvEpyZ/dvezH1DiFAY0UTLGkBBPlaofv7r+iYuuDkG87hw+GEhx0hOPQKz5uHEIxYq4kHY88ELj5dlstTU6NMjZeRHmqOq1csx+69Z6T1PseNt2Dmn8rVvGZG1suNiU5qctM4abmbvUdeUtU5rXm4/zbiQTlA5b3jhBvuZstBPxLfD9I1s3ocW9WXVAqkYw20PovEastv6LikcK2gm88GBRXztbe5HGKGzn5LzI/nUj8yt/xpy/Wv5MdqhQmgZiL46TLKPlqLLnJE53cOOoYTFBGBv6Mxhvt2sdXUn8joU1x/UcKb0lqqSlaHXxWRlRxGsPUhttnEcTe3gIKVkJX3lJbX0j4hY1E2R0tOGpfCffC+UETtyDzzCjkTda94dTWB8AuFNM8KoU+tNJeVUFDSRK/gHKfRkX/A40E9x1DdM266b2fLP39ij/8t7pQK4TDC14ImGXwYSoDNQXZokrBtZeuW39n009/4+z82sen3LauSs+3E8thjntSuJXbVKjkTlfe4d8ydPdZJvPnwhsfZz3lV0khrbwv9L6Ox36+HQ8Q1rqTf4enVWILW55AKDCcsKpkH1TU8uxZNpF8ofqefa5ITr3dk+fR4Vv7VMiQzbTwP1cFo++8c9E7iYokW697KCesXBECX6HuTTIKPPRauJ7hSP8esbJlIVasWmWzN53mkAXv2u3Ls4QfqpxT/FuJhfUu+tf5t4mEXDsGppOW1M67RvSaZmhqh9GoQwR5eOK0QDwY4Bpwi/kEFA4O9dNULoa2d9Az0U/nwHHFe5huJh4VhZmtuciLAAzeHVXHqdrbYGe1i26+/s3nHAfStVu2zt8fGVA+9fQfZqhvL/Z4pPkWHfOtete7/y4gHBfKlQd7EGmDjGs3J+zWs9pzX2hZtGzXEwxG89S2wTqmhsWeEidVydGKUyaGnXPCzwNY6lLCMBkYnJ9ceM97JYMddYgx2YReqjXiwxtX9FGfzR1e+PjfRkEV6jBdW5sEkVMzRXZBIrJMVDgFJXKkYXpM8VqMDDjXT//4MXjq6+CW+4pVg+tf8lj8tu/IpS9ksS1OVZPjrYe8dT9LTdzR9uMdp/U3oh97neVU/80sz9De84bbbJnYJE/aSdqaXFpjsrKQs3ggd3dDPxIN6CcV8vca7V/AUSM5pZ1o6jXQ4h2Sng9gHXCD1Td/apM/ySfrK7nMj1JTdm/7BT78cwvl0NjmtX/AMW0U8uIWdJSbtJY8eFNAq2DTUMlofHedYgDc20Vn0DZRSUD3AwLgElUr4JOhjzpv9iknkE17VVlL6UIvHg3qJqa5iHgfvw8jzIhnvupmYaaej4Dzeu/fhn3yeIHdnPF3COHLlBS3vzxFgaojz4WvcfFVM/ZsLhBluRdfxDFfz2zRhsxu6koZ4uELwHz9jcfQpTyt6tejob3l+OQSnfUaYJH4M4VrT18aZHM7lerg9Lg5fIx4EPUH58ctGgn6mQilb0nzCe7bpIZknfDAzCSWlfpb+qQUW57qoeHSGKGt99hwtpLZnnLnFJSSyT/mWhByFOdyMcMbG2A2vpBxqxpeYlyuYrrnPneO+2Dif5GaHgqlvf898AzTL3VQgHjJJ8bfBzMgRz5OppKWlfXe5fPkympKWxuVLSaSe8cR8526cTzwi+3Piwo/XViMbqqJGSGC5/QBOxx7yon6EWS3ONusbqxot4tUFgXhwI/q9jOH1ni3rT/iL1rWEWny6koK5zgIKLgWw/2AoN3JSOepttZxcsmgBtWyeBYlg+VhifriZupdpJPnqcWDHbvbsO4SxnTf+J26SXdlD/6wQfvGpXuH/S8SDitmWfN7cjidyg7vXRxf9iCji71dT3y+4e30P8eCNl5klluFpPHj4kKyHD3n48AHXzhwh2PbQGuLhRIgPDudLkaql1N6N5eyFdNLfdjLRV8Mz31/QD1hNPAj3oWJprIvulnpqa2qoWSml3D/thYelCbo+qRR8qKHq074P7yh4eomIA39nr5dQ34B24kEtYWmmh7q8p7x89pV4o+xb3LwQjv1vf2OfXTQxKXfI/mp80nOKGkcZmV3+JOByjgctoRaKf1OoxYrHwz+wOf6KV01aYqK+J7mkrILsOA88bLUTD4KgQjHJQHs7PZ21lL1I5Yz9Trb98Ss//dOBwxnF1HxiP4VHJysnO84TX6dwol9Nf9U1Xy2dZmq4h9bWXo1b6zKPoEY+N8RAZRY3Ii0x3ruL3Tt3snPnLrbvMMQq6jr3y3q/I4GSkD9P8Hhwwc0pmoQS6co3jFVDhby+EIzFfjtOFFRxJcoZdwd/ApJzaZjdaM1Xy6ZQdF4h0sIUn+MPyG77NHivfvfWL6tY7Cym5Iwlv/xjP4F3a6kSPj2jXmCmv4QM180cMA4jPrt64+fo1BKGPzwh3UeHf27159L7LnokU3SVpJNou4t/6sdxv2aA8fUCUb1A27OzxDkasccijiejUk14h0oywUhzIS+uxBDqbIS+rhmOoYlczqnRZCoWFH41cnqLM0lPiCbs/Bu6JEoN+SCw4kONheRej8HHaB8GxjY4hZ7hwqP31A4voVCrmKy8T2Zy1JpJRlDCQ17VDHxMTPlniAchrGWM+gcxHHa0xtQ2kGOpV7mSEo2vmT5OIeeIv/WUgjR/nDUx/AYcFKzVv21ms44xFuZmmFlGkJhdQdPqfrr+cQlO1YoFxupzyE46jIulK2eSAzA7YISxiTXOQaEERF3gRl4T7eNzDFVmcuewIVu2HMA8pZ76oSVU6hm6SzM577Qfg7gSPvR9dJ3VWJgUSBfnWZifpu3lBZID7NjnksbbwQlGF6Ustr8kVwi12O1M7IvqZUtyq2CFKqYs9zSuf2zC9kg6N3IbaBW2t7bR2tbP2IJ0+bvuqzweTHyEsKjuZfmn+VJIE48irfDwjiI4MYM3KW4c2m1NREYplcNrk0xpYFFN0FVymzPWu9GJyKX630w8qEYLeBjniqmOEbqhD/gwLmFxfV9e9XyE+P6Zrlwu2G3lwAFPIm4W0yQRJpHC+zTNh5vBBAmTHscTpH6YZFYu5Ez//p96tpbqRydx2WNK6L0qimoq3wC+AwAAIABJREFUeX4xnCBbM/QjH/9/zL2HWxTZur79R337t/cEZ8wJURAl55xzBhEByUEFBSSIIAqCEgVBQEQkSlBBsoDknDPdfX9XNRnBcebsOWf6uhZdXaGprlpVtda93vd5qPvcwde2IjLv38ReWxNNza2igZLcOc4eO8JPxy+iqKaBxtYyLX0MLW5wv2aKgbk1pge+0PGxkab+WRZFEtanmyhP9OGauQ1m0fX0L6yx9DmTp2EBuPmn8mZGsvFs/+GIB2FkvpI38c5onT7C7z//B1mnXeBhcZy+FCtMNfTQsbvJneQDhEuT43hw/wamF46ieSONZ/X7nJwkU7S9vEe4owlqvoV01sQT5uZN0INXVIxMsTD4npfZxVQ3faV/eITxgU5attM3hBQOobTS2t7DyMw4vS3vqamspbppQCpcfAgn2jmR65PM9b/klpYcGrInOKfhhH1UOX2CUcjOWodMrSNaGWOg5gn3nDTRUNFB0z6E+KfpPHkUT+y9W4T4e+JuY4jOxd/55efjyNvGkFT+heHeH9V4kEjDxL+JeBCvIR6vojApgpAbB6REenpy4+YtAmOL+Ty9JO2M/Qh40DZ0xTXskbT9lZmZQWbmMyLdddHXs9wV8aCEsdtdogvbGO/7SNk9O1zuv6G+Z5T24njuOWh9Ax4QUi1m++lsaeLTx13tr7pqquOsMVLURMc5nIcVjbvaZg2Up4dzx1qdYxe9Ses5BDxI5hjr/kht4R/ke2fFEuZmiKasPJfsYkhKyyTn0PbXCwpeV1Hbu8yy1Jb5kCogzJbMszJTQ6LFRfSdooh9/XVXtOF3ttu/aBM82CnrYZjYc4BF3w+IS4qnWJ7MI1RNBqMDwYMeVnbhxNTtPLhEfYVkhTigp25NcGkND921MDR0wyuxnHZBiGnfS7I0xHxzFA7yV3G8W0TRgYKgYlYmv9CVcwMLZW1cowp43dXL1w8CePgdpes5FEstsA8SlxSEZJtoDFdD7soWeBCzPPmF9kwPrK5cRNPMjWthUcRFBhPqYYjiZVOpfW+18MwUno3iVWZ7Kil+FISXpR5aGroYeYbgZauPiaEplp6RJLxqon9JsqN3JfzO3eDB1Q8n72C8rBxwfVRPx0Q3r2NcuGbrjOWtXNpybuDkEETsy0+0j36huyoG57MnMI9+T1VXC++/AQ8SloY+UPfUC6PzJ1Gy9MErJIK7wddwtzHiooY/6bUfKX/3jqrKBpoHJ1mZ/UJt8nWuWZiiq62Hmaku8hoeRBU00za+tCvVY9dJ2gIPJ46gGyyIh859++z6Q3FJMazW8Oym0I7dCx5Ey7PMTowwtK1vMcTQ+IxUt2n3/Xa9K5fMIAc0NL142CPadLX4I3FJCZKlNmqLC8l7UUHj10kWxcKz7SMl0c7Y65ui655IfVMxqYlx3L93j3s/VCKJT39F0ecZxIIOYrcAHkwwsQzkXnnftobHbj2PH5ke6G3ma91dnK/KY30geBAyqMcZaykkzc9QKupvei2GpNJ2pK6gu07b/knxaBmFAnhQtCagdpWRv+Jesv9L/8Ln74AHYXBtiJGWN6Qm5VFT/4RQ+y1Xi3kkoil63xdRmBbP/bCb3HCyxFxfA01dS+y9wolOyeVVeQ019U10ji4yv8cm7TDwIGKs/AFxzuqck1FC13p3ioI5pkYaXDl1AmXvPIqbhcbG98CDcMF38e5RAD7mRhjaXNslWOXFNWdbrM3NUfPI4v3AGHXPA9gAD1UsS5YoD9PG0jmQoKwmxg4FD4cccckENYkeuJuaohPyVhqeud3gEAvK1wVEqf7EZbvvgAfxDFP970i/YYu73Xesy6xMMdFX4sJP/x9HZVRR0zPDanfY4Z5pG6yt7QnJ+kzT0AoS0WEaD8tSu8B0FxVMHW5x+2XvZv7sX9B4+N8CD9unQsxMWwHZ4c7oK2pwzU6ZM6c0MPWJ4mHxjhhmaXEcQTaa6KgYY3Enn5LXm8ve1FDf0ie9gHff8ASwIVqZZ3rkK90t9dSUviArOZq7AR642Zlham6Ho6s719wEAUczLO1duOYrCPmlkVFQRkV9My1fBhieXGBxXz7ZH4KHS7oEvHrFTSsLrvkn8LhmaC+B3/ztktVJ1jtj8dRUxy7wOTmdPwAeJIuMfMjlqZMCv56y4M7rTjoFCirk9H3J5Zbir8jq3+JBSce31p6ImG0vpcRPlSO/aHIzv4Wmya98LrqHx5XzHDF9wpuO8b3WUtJ9FTH6LoF4ezXOKDgR3brI8NICgw15ZITZY6ahhIKGBc4hyTyvaKdnenfI+iqt2WEEmmlw2SGV5oX17e+XrM4xP/CBiudR3HIzxkDoXJl54ZdcRe/yGkO1aaSEX8fe1hZrCxP05H7nlE4IMa9apYr4gobJt64WP5hqIbXd7OZVsCnmyldR0DLDwdUOM9VzyFw2x/NBCRWtXXSXp5D0IAxXbVnkhdQQc28iIsO576qC3FkTXB+8pm53Cuh2vRYao+tIlobprMoi5ZYnHi438Ih4TmNxAFbqRlg4+xAUF0GQhyu2Hg/I+/CJkrQw/LVPcOTkRU47ZFPWOcmyaIj2tw8I1FLAIK6Vz8OC8NH+18GuFuvdrygKd0D+oi0RdUN0jM0yOzvL7FQ7PY0xOJw4illwAbmNExvzZ+eYnRXsDoU0kg1xzK1Ui8PBgyf2/nFkeumh7vKY3E/DTOy0aXd2VDzOl8pkwrRlkPMqpqF7RrrsvxXxsPLpEfedVZG9YorZwzYmloWokO+91lie6qI8QBF1WWXMw7Io2sxVZrWZDC9ddBV0MLj5nMaFQxxkDv16Cau9r3l93x2lK+4kts4wvDRG4z5xSdFsOx9LM0i6t88dZctJ5Zv3KKLj0yjqXGByWYJoXcT62rpUbEtoMHcURRLqYIahdTCx1WPMrYpZqX9AtLM12taR5E6KN0TYNsGDrZoxVoktdA+OMTExIR05nZ4VbClBiCoSz3bSkHmbQBstFPTM0T3/Gypuj3ciHrbAg7Yl1v4Pya6to65uX3n/hvKiGK4pnEDP61vwIJlppDjmBi5mTnjkdDJWHc4NQ0ucQjMoGVtDvDbDZFsl7wpzyXr+nOfbJZPXHwboGZllfnqC4YlZ1oYrefU0g9zXH2g/IMXmoNO1PvOFgTdBGF82wtXVBGMLC/Ts7pHZvcLSH3Y4l1mZaaEkzBijy2c5e0kdDQt3fG7e5Ka0+OLr64d/QBCBQcGEumiipmGDe3QBb+prf1Bc8hDwIFpG9PUpd+20UJNXRtVwb7vDTE8NNUU1ZI1iKB2eQzDaORw8zLM8XcvTa0bYmVtj7XR9T/vL1doIY1sfbjx8R7801WIHPAy1lfPc4TQXXdMpaR44HDwcdPAFzLcyx1iOC7ZaFtjeyaVqT+NasODO4LmnLse/Bx5Eg7SWpRJ77Q+sY60M0FK8wNmjxzh22QBjc8s9aR17bWdtcfW9S8y7aWa2lAsFbbPFaabaq6goe72pyVJMcVE2L7NCsZM/iZKRJzeisyiS6rUI2iw7paT0HW+qOxleXDu4w/h/DR5UzQgseMlNIwPcQ5+S+WF0+3m9+/RJlgaZ/3QXO3lFHO8WU9S73WLeXk20MMTX6qfcs7iCutU9Hr39wuDCGP0f0gm5+guyWi54BEfxMPEBUWHeOFz5mWNXLHH2i+BBYiJxd4MIMJbhpKzLZsSDmMWhzzRE6aKlqIymiR32rg44mmtw+cwZrtgn8kxwgZocZqClkjc5ScT422NlqI+B5TV8YjIpbmzj47tsUiM8cLUyw9j2JqEpb2non9uxTt8NHq7fJzQ2kQRvC5R1PIjMekyggxH2dkJ6YRPDtQ/wN9XF2jOGBxnPeJ3mjcpRJbxfdPF5tIu6b8CDmIWuMt5GmiN7Vh5NCxcc7C0w1VTk8lVDjKLf0zKxuBEBsCzoaa0jWR6l7UUAHjqXOPXTf/j1xAXOGEaQ1jDEsFQvZvuQ70z8reBBzGx7GW9SIwjw88PPz19aQhJfUfxpdJfYuWBU+Jx0XztUtP1JGRZvaB+J/wg8CG2nBaYnppmcWmRdsoZkZYim7GC8zAwwsg8j6mU9002PuePljL2lJZb7i7kBepqC5o8iKvqWmJoL61jjGvyQpIpxRNvgwQxLx3s8appBNFZL8fMnPHrwo5EPCTzNr6DmywjrfYncULmC3SHgQRjMENLlhAGoZ7cdsDE2xdztHg9fd0htZg97zIhH3lAY5Yaekh0hDWv/TPCwkWMliFjss9N8O4tkfYj3j3zwttRFU0MbXRMb7D1DuJtSTEXbCONTAww35xHrE0hKxSDde3IcDwcPo2+iibLT5PxVe0Jf5JO57RTwlCdxXpid/A9XPLJ5KbWN/D54kKyP0lldyIvk+I0wlq1wls33+MQUEnIb6ZmaoXEPeFjkbZg2VlLw0PznwYPoC6+jHHEwssH0fqOUVG837H8UPEhmmR2qI//OTUJ8vfH2PqR4ueJip8vlX/7FycsGGNm643XYut4+eHv7EvOqk7bR1cPBg3gByUIF8ZaKmDpHEl06LIU8f0lc8n8dPIgYKnvIowA3TK7FUPHIAuWL8shrGGK2xwLOBG2F88icuYS8vuOONZxbCJGpZTTOS+NyEM0NM9DVREP1W8peZfMs6T5Rt/zwdrHGUrB5M7bF0ecucc/fUPmxhZamGsqzEoj2d8bRTHCMMMPczh1Pv1BC78bx8GkueSU1NLYPMLayIV65BR7MDa8TWDzO5PSMtNM23fGS3HBHNOQMCCip5WH0E/JelVHZ0kFzcxdDi4Ja9M6zQbIyyXr7fa5raGAXmEFu1x+BBwlrM+3UZ4XhpirHebNEXrZvqj2Lhhltf4zn2Z+5aBZDyruvB0TmCDoPldTe1eO3n+VwSv1I3UArjXnB2MrIcNwxj/c931qCCjfNufpUUjw0OSU4kVTO0z87TVfxQ+I8zDGw8SH4cSm1vdPM7AGWwm89HDxIj4Q08mWavroXPIvwxM3xGo6hL2lZWGNquIP2D9WUvy2j+EU6MZankTMMJrqg5X8OHoTHo2iImtQIogNv4hvgR6CXNWqnjqPkmEBqdZ8U3AiCnevj70hy1cHMNpDgnA7m5sdYLnTHVNkG9++BB0Hsc7Sc9CAHHK1c8Yh8QVnHEIv1EXgaOeETV8DrL520lz7htn8ceaXPuB/ojOXVC6ga6HJSKYT0+j5GF9r5+PIOLlfVcMoepmvy20YebIIHVyMumsXx9ksnn/vGGK3PouCWOWdPaWAflcrDNCGCLJPM5wk8uu+M5m+/omIZhN/9ZxvzpRFmWRTUfKFnbBnJrogHVZs7xOQ3MSGAi5lxZiffk3JNCwubG7hEZFKVFsWD0hbe132kvauf/j0ASuhhjNFVkUSolgzy3iUHgIcjyBn4EZCQT/l2asHuFIQKKspe8TbZBROFsyjtcbWQMP3mDiHm8shoOOL1anYfPN+57namJKzPj9CdZILhZVm0PB/xVEjMFi8hmS0g2kqJq4p22MdWMSW1ftzZEvE6wqjP5PgYY6OjjEqL4BYywcTchvbHcnc5756EY++ZSvXUMnPr49+AB7EASNdWWVleZvmbssTSwizT3Q3U13ygqWuQ8aVlllc2oNBGupHQSBMg6ySjzTnEuehgaOCMW/w7vixtpFWsHgoenLGQu4qqawwJj55sWFCnPiOnqJqGEeFeJWGlM5uUYGdMzF24mZJCoPop9K8fAB7UddCy8iQ47gDbufhIosKvYXT+KBqe+8GDhMWWpyRcN0dbx5WQl3XUZXljr6GPmUc0ybVddHX2MPk+hmAnCwwNTLEUbFHtzNFXUcY1/h355Y18ririWVkb0x9j8TN1xDM8h/JN7ZtdZ+3bSckqc92VvAvTRU4zhMScR8TfdsFC1xrHp10ML6x/P+pBsszKbAclkZ4EeF7DwyeQ4DtR3H/wiEcJMcQ9TCNTOJ5dAwyNjjH5QRCB9SYoLo/8ypr/IXhYYr3nMaFmKmjqOOJ6/8Uep6aUOx646ylzQuMeRUOz0pSww8HDMqvz3bzPeczTh4e0v1LyyKvqYGxulLKgLfDQigAenjmc5pLrs78AHgThzGk6Es0w07LD9X4xTbvz8/lB8CAe4UtNLqmhQpvpkLaXMP+GPebaCsieOMVJNTtcrt34bvsr6F4SKXWCxsrWg1vM8ng3HZmB3LzmjIODg7TY21pgbXyVc0d+45yCFlpmdtvLNtaxx87GCitrZyxvpFM1On+wbev/CnjQxcIqhIiy8U3YPMtUaxYp/jboqlkR/Po9SXeTePmmkurPHbS09TKyvBe8ShYHmf8Qga2cEk73iineBx4E96GRT3k8DzZD/pgMhvcqqOqZRyQe3wAPCj9xRuYKV9V0MDDQR0cYvDjxb34+cYnLKoLzlQF6WmqoyR7j13POm+BBwspUP72FkdyLiCA8OoZ7IR5cN1Pm5HkDfLJb+TzQT29DPulhrtgaqKOhaYCZ6y2is2toHtsSrVtjtreG0uQAPEw0uaxiQ8CzRpqHhU7u3ogHy5sZPC1vobM0Fh8dObQsjFC8pIaleyypbYuIVwaoS3LH2cQSS3MLvN1NOXHelcTGIQYWuw8ADxJWhz/TWhCLr38Y4fdjuONthaWuFipmoaS0rzAjpA+I11iZH2fky0c+vEkj3tcaS31dtDQ00dPTQVXPCZ+YbPIqm+kYmGBq05p5+wb3N4OHycZsMsNdMDM2wthIH+Xzv3HOKJiwzA/MTglRTZ+lUWkfs+9w29aQSyruRNY0U9P0mc9N5eTEeeGkJs855zQK3tXT9PkzLR299IxsRXNu/hJB/H32K73VyYSYXEZdx5UbSZW0Tc6y3veWl+nJJB6UInH3Om6mSlw4KYuyawy3IwWY8IDH2W8oa5tFLJEgGijgaagPviEp5LSOsfYlnXvXbLA00Edf/4Cip4uOlioqcmc4+fsRjp64gJbbfZLejyEazibSwYbA+BJeHxj9s3VmxCz0lJJzzxlbQ1MM3JMoHRExd0g+oHiolIJIN/SUHbj9cY3Rpe2e6dYX/q+8fzfiYXsPBDG3PXaaC4hWZul+9YDk+Dhi0oopb+pjfH6R+YVFFhcXmR9p4OMLXzR/PoFtfD3vuuZYFBo5gn3IoakWIqTgwcGYK8aRFK+wM8oqHmWwOQ0/2Z9Q9fwj8CAcTBELE0MM9ffS09NzSBF8vb8yMDHN+1RfAoRUi3tCxMM8ZaFaWLkEE5z9+c+Dh+VaMgMtsDRwwfVZj5TYbZ/eHwUP2wf/Dyb+Bo0HwaJyrScRb/XLmHk+JqVR8LYXfsE/PeJBzOrSHCO1L3lXmEdadQeTGZZoy8kjp66PyZ48XmO0FM4hc0YWOV17HLeWuQZzN+UNDXMbv3i5LYenYfYYKF3i/KkTHPvtKGfkVdGy9OBmVDq5wmjDzByz8/PMb5dZ5ufH6Gl8RW5iGAEu5hirK3Dh9ClOnzzJaVl9rIKeUTomlorZbIAHEwxVzXGIKd6OvnidFUmEuwnK8hsaD1+nV1nsLed1UhB2Vr4kfZxkaEG0LYgjBQ8t0Xioa2AXlMELqUDT4fVHsjbPUFUyCdd0uKRgintuH91Tm9EFogGGWx/icvxn5KwTSKse2EOet751ta+WjzFG/P7TOWwSG6jubaIuxx/zMxc441FE/dfdLjBbW8HSx2c899bm1AV9PEtm+TorZn2yl0EhnLB7bkPEbmf1XVN/AB52rSlaGGZ8sIfPvQtS8bydRWIWx7so972AqlnIPvDQTa6PDtb2AQRkd7Em+cGIh80vF/LyJRIRy0MNfHzizNWzOvhlfuLjxEbnXrQwxmSxJ9YqKpj4JJP2aYq50V4mcpwxUrTEOTqf8onddWmRpZUN1wHJ+jyivmxiA+8Ql1bBB8FWdmmc2dpYbl0PJTqzhqb5Dc2K9bU1FhoSiPJwxNzmGvGZ4eieMyW8sJmmr9VUpvhiJG9OWM0s/TtCBNuHSCJa5uvrWKKctDmr6U3CozACEwopfhpDbqA2v/77F34XhJBOneKUtJzgxPEj/Pyvf/HTr0el4kob84XlZ1H1ziS3URgZ2BGXVDZ048a9NEqklrzFlJY8IcjsKrqWfvhndrK6usDa3AdehLrg4x9DzJs+5tc3rkvpjopH6Xr3kGBNGeRvltLYsz/i4VdO/HqUoydOc/bs2YPLmdOcPXmEX/7zE5esd9tpihjMD8DX8BKXdD0Iq1pl8ZCH+fZBEzQKFsaZyLTH9OpZlJzjeVi/CqJ5RIMphBgrcFnjGh6pzd/YZUmWp1noraYw6xnPNu2oBVvq9Ixc8uqGGJ9bRbwyw9RIn1SNf25dLG10b0c83KphZGae5TVBlFIQg9yMRBGgznaZYWq4h7ZkO6wMXfFKeEXd5Bxz80usiLYELAXhtEkmvpSR5qaI0hkZtK4nktay1ciGw8GDE4a//8RPv53YqRenL6FmFUh0wyoLa2IW6h/xODpK6k7RNlhGlO4ZDA8ADyZKspyWkUdRQwONb4oqaipynD3yC1c99oMHESNFtwi1VueCgi6W19y4Zq3C5bMyXFLSw8QjjMCI5zS88sVW3xwz1zDiXuaQ+/we7ldPYxNeSGLSI9LCnFDxL6avNhrfPwEeJGujdFc9IUzrPIreBbxt76a5IJJbFppcsEymanhBavm7U2cOmBJ0a9bWEAlCj9LFgu7NAhOViSREJpP6upmv0tuJoPkySk9HL30DE0z8T8UlBSgqBQ/qmDrdJ6Fhd499ncGKxzx01vwB8CBhXdBAGv5KX+/3219fB4boGxrgTcAWeGhhsK2MdPvTyLlnUPJ58E9GPIhYWxyhIkQFfX13fB5XS1Ncdo7yD4KHnQ2+P/U/1njYBA9ZQfh6uGyKXztgZ2mEieJxjhw5j4K2EWa7hbGlAtkOWBmooSYnz1HlW+R/nT5YA+J/BTxoY6jjiPuD4m179dcZtwkSBLw1XbndsMrY/BqLXcXkRvvj7HKb1JZZJld2Us0kCwPM19/B+pISTpEllGxU8I1jL15loa+MF3ft0TrxK//+9TKmkUWUto8xtzwsTbUQxCXljP0JiUsn7+UL0hPD8VH/lZPq7vhGPSH3ZT7ZKbHE2F/i7KXd4pI7p1e8MkhTXiRBJppcsk+nanCWualG3j32xV5VGRXLAO5l1dM0NMfSFjfa2Zz16S46iqPx1LiImvNjchsGpalZu1MtNlwtllid+kJPjhsml47y+xE1zIIyeTe3AX3Fk9VkBllicP4IR04qcNQwifK+aebXew4AD7t2QIhTW+6mLMadazbO2MXWMCaWsLa6yMJ4N22VWaSEOmJy+RTnL2lh7p9CWvE76l4/JNTiKgrnL6Fs7IZXbDYF9d0MTS+wtL55jv5W8CA4x80xOz7MYL9wz2gm3eMqBg4hhD4uoKvqEX5O9jjZ22Ojr4SS7DmOnr6KrvBZaj1uiZHmFeROHOWInAGmFjZSO3JH3/vcfdG286wVr7M21093dSrheic4feQo6j7PyGha2n0QD5wWj5fz6r4Degr6OBcsM/SNDaUEydwXWqrfUVn9mc7JJcTzvbQ3vqd2j96SMPDxjrdvSigpyCHzcRR3XHRQl7uIoq4TPomvqRtahIUOGkqKqPrYS68QWvbdl4SlvncUJQTgbOtL7MdFxoUKKmhjrK2wsrTE0ppko68x+JqCe27oqThx9/M6Y9JU0O9++d+y8MfBw2A2sc7aGJn7E1wwyHh9DhnJccTcjyY6Jo74hIebdkmCymgSSXGh3PI0QPanI6jYh+F/L4nkzGIKG4akYfuSA8Ul/1vgYU06ml8UYoKVyjnOnDlzSBEaozJohxYQHeCKvxQ8vGNZNEyBrzpWbre5nd/B+J9MtRAP5RDvpo+R2U1CyzYs3rbP3j8ePEhYneymJ1EQ9tPGMaaIcqlF0D8dPAiStGPUZSWSkVXEm5YRZsb7mRLAw2UL3ARxyfEZZmY2y1gpqb5mWO8Wl5Qum2N+cWU7bFEy+4mqvFQSoh/w8MlTksI8CfD14aZfAAHBoYTdDufugflgd4m4c4uwkCAC/f3wD7pFWMprSgpyycstpqSmk/H1jaiKDfBggOIvP/PL0V1KuMd/5/cjJzglt+FqMTAvRjzfSWPubdyU5ZExjefF51EmNtuHkuUJ1prv4a6uiV1wJnnbAk3btW/XhJi51gwSbxigpWqMUWgxbbNrLG+JTwrgoSUB52M/I2+TSHr14IHgYa2vlk+xxvz+0xmsExqo6vlEXbYf5qcvcPb6axq+zh44wrf86TkZ3tqclNHFo2iWr8LNVRj1XV9jdXfnctceb0z+OHiQChSJ1ln7Ju7sEPAg1RnoIPuGNlb2gQTmfPnT4EG6j+ujfHn3iHDjq1z1eCFNb1iQPjvWWRxvo+TGBZRkFVA1c8U7PJLI8FDuOitz6fQVVExc8LwbRVTUZrmfTHZFCx3SuGYxiBaYm5ljfmGFtfUpZgfLSHa1IOhhGWUds9v1VohrF41VUfIin6fZpXR9KSRcQ0aaP5v3Ip38cBeuqPqTMbjExJ7AGOE6X2dhpJWKRz5cUz/Bv38+ytHT8mgHZJH1JIYMfzNOyzkQUVROcXUNNTU11FTmU5Thi/HRI2i7xhGdWbExX1hWU0tj5wijs6t7xCUVTh/hyO/HNtWfT3BSEBH8+SdO6/viny0ce8H3eoKPj69xzVAbbedonnVsdJKlx1k0Qmd5AoHqMij4vqGxZ1Y6eyfV4lfOyemhZ30Nbx8ffA4qXtfxtlNH/tQxFPaAh3UG8/zxNZBDTs+T2zWr0of3Rh087K8E8eI4U9lOG+DBKY6EuhUQgFFvMoGGl1HQuo5nWstOY2jzq8RTHQwWeqN38Sxnjh/jmKByfew4p2SuohZWycevc9KGhODmsra+KdYq3op40Ect+DXtja+p+9xLzesCCuJu4u3ltSe83cvLixsebrjoXeD8aTlpOpC9dzCBd57xdlhIC4aoAAAgAElEQVSw7JQgWR1noDGLJBclLh77mZ/+I4Oebyo5295z3wMPLlgpaWNwu4g3lbW8l6ZINPCprYeBOYlUhFI885XBgUG6h0eZGSnjvt7B4MFU1xHX6HxqRvcJ6ApiZeNf+Nr6nECV0xh5p3+j8bDcmkf2fW9srKyxtbXG1ugKsifPcF5eTaobcT0whdrCm9gauuARlU/17DiT/aXc15fB5d7/DDys9r+jPN4ZpXOaeBb00T4ldLiKKLprzaXzxgSXDdM9+0cNSaEDsoEcNqqHILo5RV+aAzb6zlxPKOXTNhMQs76+jki0zsI/BTxIFhhpKSLZSgYFWcFh57D21znktG2wiK0i47rCpsbDR/qaC0g0O4WiVz5lbSN/DjwILkWzTTyzv4i6eRC3c1tY2HO5/tPAg3BZr7O+NMfszAzT04LI9ST9TWUUeMkjc9WDiLz3tE7uiF9vrDNBx+sk4p00OPZ/Dh7UufzLL/y6r+3y268nuaDuxu3GNWkal3iuhconfjiqqiBvm0xp7ywzm88eATzMvb+F5SVlnKNKeC0IoggviYj1sWpyb9lIO+m//Oc//Otfv/LbSXl0PB+SWllPU8OGxoPitefkNwywsDzLwOdSnlj+xnnLGJ5VdjGzvMhkdz01t1T3iktu1w0xMx/TeHTTBh2jm8R+mJHag4vFK8yND9PX1UXf5Byz0pTBLSC4vfHGhLRjK1hs1tPcN8XE4qYY965Uiy07TYlokZWpjzx1uozqRU3MgzN5N7l5zYuXmWhIIcFDgwvnVJAPqKJjbBGR6PvgQdCgmK6OJMjOFvubyeT1LCESrzFd/4TY6waoXTrPuUtqaDndJrG0lfbhOanD1uryDLMjLVSkhhFgo7mx3kUNVO0SKOia2mgf/M3gQegki8UiRKJV1lZGeeWriqnTJnioSMTX3hZHOxsMVS4ie/YMxy9pY2lri42tLba25hhqKHDpxFF+vaSHsblw37fD/mYU4bmt289a0UwnH19EcFNPht9/+Q///tcx9AJzeNG+fTPdd0J3Pv4xeBDqqqB5uLLtEiWtu2uCa9SWu9Tm+8Iw3TU5PA11xFJdgfNK1njGFfLmcz/jCyusSlNSNwYQVtfWtzXfdvbm2ymJaIWFmSlGh8ek0Q5Swee1KUZaK3ibk0FWyzozQirlQAn5d93QVXElqm2d8X8qeJCsLbAyXEt+hDXGl0+jaOpHQHYbXWnuuFkboaeri+5BRVsV1Stn+f3//VsqnqakoYeBUyj+6cJoj4S/FzyIkEhm6SzLIPvxZphfbCSx0V6YXz6Niq4ttn73N9MvHvD0bQt5MTc2Ix5KWVpt5JGdItYescS+HWLyT4EHMfPVEQSY62PgFM1TqYfcroryDwcP0jCk8ji8VU5wxeQO8SUdDEvD3Tc6JF2pTjgZ6KHlcIvk0goqKyupqmmhpTCGB57myKr68HhItN0ZZjvV4t9cVNBDz9wRV1fXvUWICNCS5+yRoxxTssTCznnvcmF9FxO0Lp9H4UA7TaGvNId44hUxDsa4Bz/lWeMMazMDm+DBhht/VVxSNM/02DADfb10tlTy3F0BAwMTzGxd9vhdeziYSDUF1LVNsHLz3PHLvu6Gk40pxvpG6EVU86FrgPGJKaZml7Y78lsRDwbKJlhHvOBFXr40xDUv9RYhTgZc3bTTHBBGpkWLzPRUUPbAFY0LiliF5VHcMiF1hZCCh08RuKppYheSRf6BIVobrhHznXkkeRtjrG+DXfAzSjqnWNqy7ROqq2iIkbZHeJz6mUuW8aRWbtnI7qrLgsZKbzUNkQb8/rMMdo8bqen7TMOLQKzPXeCkayF1vXsV5ze2lrDQmEaapyanZA3xLpujb+4HGuPSjf8EeNi9q3umDwMPK7D2kaduGljYhxCS/5X1PxnxIKSCTDS94HmYM7oGN4iv6aO9u53Wugqqaxtp7PpKQ6w1TqYG6BubYyXYUFpbYKslw6mjZ5FR1ELfxg47Yb5QHHyIzKqmQfBwF68iWWyjtiCTZ4+SSHpwl6hgezTPy6Bt58fNiIQN6JuUTNKj57xubKOpZ4yRsUnmJ9qoCFLF2DGcmzfcCfWw4IpjGk2zqzs5t2tTzH0t43nYdTysjdC5KsP5U+c4fsWeoPTXvPk8QFtJIo+9zLigG07hwCTDggPMwgILM518/RiL48mjmAcX8uLj1MZ8YdnCAotC1MY+jQdlXUfcw5J4IU2nyyX/xQNuGimgbeEnBQ/SZptknbmv73hx1xU7PSMMfDOpGV2WWl8hGqbj7QP8VWW44ldGY+9+8HAEBYsIIrNqae/qouug0vqBtnwfbJRlUN6TaiFmojiUQBN5Lmi54v9m6QcjHsYYTLXCWEEGdfeHPBZ6iKIFxKPPuW1yBQUVF1yTGhHsxfd0LTcjHgoyhYiHpzxNukWIux7yx85x2e8tDZtQZacai1iZaaMs4TqOSqc5rqCPiYERN5Lekv4sg6w7TjhIR4PspaM/9pvTdjaWWKie4dTx88gq6WDscJ1rNx9RNLDA1NoKQ/XZPPUzQ0NVF2cPU5QuqmHpn0rurtDnwyMeXLDVMMPmSTeDk3MsLG5EPy5JbWI3f69ohTWpb/oM898BDyYaBug7BxP9fDOVR5qyszmd8ZiURD+sZI+hfYC4pHhhjNG+Tlqam2n+8J4PWV7Yquti6n6Ph5VttLZ2MvYuCBc9PTSNHLkecYvbwW4YXziO+e2Cvx7xIBqj7VU0t611uWRyn6KeWaaEkaaFHtpK7uOheAGVa88pbBnbGAndOZl7pwTgNj9Ab3sTHxrqqa+v531VGQWhumhdMcDUO5b02o359fWNNPeMMTa38s8BD6wyN9pJQ/YDkhI21dpjbxF8wxLt07+jaBOM7+0Y4uLiSXyazfOKVgpuXpWCh6iXFXTUPSFI5TQ6t6uo7p6i4zBxyb1HbePT+gQL/VkEqZ5F1/UhjypHtp+zGyv888DDNz9DssRYawmpVqeRNY0l4/1Gmt7e9USM1KbzxF3rB8DDdcxPn+S0qjW2ji772lYuuDoboCp7ktOn5JDTtsVlf/vM1QFnB8Gx6DeUDrTT1EZf0xanyJ3UnLwngdy00kVNsNNsXGNK6OCIFphoLyY/0hG1i2rY3CulsntaKjosme9ntiYU80sqOEe/prRfEAufZ3W8lpxQayx1DDEx0MPeVJ1fftXE3scWU3MzbL1DCHoQR4ji7yj+aXHJzSMqEUm1XHJvO+PicAPPR1X0TIzR21JL1ftmPtXX0lmRRuSdO4TfucOd75W7cdxLek3T2AIbgw37Uy0yef5J0FCbZmUoj1t6slw+fpKLBl74Z7QwJYwtIGHxczpPvDQ4/bsMx3WjyG+bYHL5oFSLjd8gWZtjdfgtT64bYu9xj+j8T/SNDtL7sZyip5HcCfDD28uPoFvhRCY8JaPgDW/eVVJVVUVVVSVVFW94lZ3C47gI7t4KIuhWFMEPK2kbX9xwHJOCh3hcjv2bC1cMMLA8qB1vhqH6Jc7+dpwTylZY2x/UjjdGU+4sCrp7xSV36rYIsWiCYj9VzJxCCMtsZHayn47PTXz+mE/8dQOMdcwwu13AR+H+LpRPb8mJ9cJRVY6zTk95WV5HU3Mznzt66ZamWkhg7Qs1T0PwMdFFXduYG6EOqJ6RxTIolxc/IML+Q+Bh50ccPiURMVf3iFgvW0wtXLl+7ykvKptp7Z9iZlnQVvrrL6kL1voqkrUJet/n8TzSm2vWJpja+XCnYpaRBRHrA8XkR7ihq+pOTJeI8b/q4vHXd1O65XciHjbyVQc/FpBxxwlrLXlkjv3OFdPNiIeGXHKfpfAoPpb42FhiEpNJTt5V4sO4fV0f2X8fQdk2jIDIRzzJKuFV45D0wvp7wcNGJ3lxapDPZZlkpz8lpaia3i953Da8jK1nHLElPQwMDDAwMMjI9CyfC+J5eDcMn+S3LHxN4aa2MnaBqaTUjzHY8ZG3t/RwupPL6+a9Yid7j78IJLM0P7TDVtccy+Asavbnhf5TwYNPLs+LKqnPv0+EmwEXLxrj9biK+v75XQq9YiYqYwh3NkRDTQdjOyecnJxwvhbLk/s3Cbv+ffAgK6eBhoHFt+JLVgboqMhy+tffOKpgiKHpAQJNVrqoXjyL/IHgQQhxHmbmnR92KlrY38rmZc8K61vgQV4HE697PCjcEWUqLowhwFoDbWVDzG7l8Kpoc1lJBbVNPQwu7Qrplp7kFWYG6nhudYSrBtfwiXxK4S6Rp8IH7thra6FqcI3grOIdIaiiLJIjPLBTu4ysXyWfBua/iQDY0niwMPIipEyIuNjIz17sLaYg0hnNyxYEVi8iBQ9Ch0UIhW4t5ImbEpp6rvg+rqBxdFWqdLv24TbOalrYh2bzcldnYaOeShAtjjHa/JKn/iYY61vhHPKU3IYBZoSqu7syi8cZ78okWP4XLhrfJfHNlwMELcXMd76lPESTI78o4pHZzIfRbpoKw3GTP8/v1s951zVxgBq3iInqRyS6qHNazoZbDQsM7hEA270j+6f/TvCwmVZhp4aFYwQRr0cQ/TB4EI6eiNneCgqinLHVUkJWy5Xg6GhuBfngfd2X0LgcSr7MMtr8htKCXHKys8nNzSXn2RMyA3RQlFFDxymIiIxccnKyyXieQVZOEZXNXxmQCn4uI5mtJT/+NiHXXXC0NkT36nlOnldGy9QeB1cPPNydcLEzR1vdilsvW2iWerAJI6bjjJf542pmgc5VBQwsbLGI/8T4sminPq6NM91TQKyzDa7OHjia66CvpsoF42hKhhaZWV+lT0i/cDHkklUSDf1dNLc287Gxkca6It7kBmBy7Dd03B8Sm1tLozD/YxOfuieZW94c/dmVaqFme5cHRe3MS7UI5lhe+Ej6dWEkYws8bJ570SyD9Rk89rVCTckSr2dNtI4tsSYapP1tHL4qMlwNKOdDrxCrCjsRD7+h6PyEtFrBqvHgl5A7vPYxAg/tS6jtAQ8SFt/HcddOiQvK1jg8G5TmZe+5Rr75SjGrs/18iNBA++IVDP3TeSFcg5JlJAtvSXBQQ0XBHMs7xd86HAj2YasLzE4LVrqTTPSVU/rIHa3j51DYBA/ixQnGuxupLHlB9tOHxEX44GKkhMLZUxxXtMDZL4qUt+00NjXT9Cab9ORk0vNekldQSGHhRnmZ84yUG6ooyelg6H6b+JellLz9yJfZNZbEqwzV5fHifiDe959Rlh+Cg6Yetn8CPNhpWmCfPsTU8mZUxjfHaHOGePb74EFVlau6Vrj4CmJj+8sNvDws0Dj1G2rX96da7P2HQjrZ2od7eG2KSxaPriOam2ChKgQ3PR3UdK1wDvTD38cB/fPHMbv9VyMe1plpz+dpgA0mutbYP26mb35to9MrWWK6u5yiEH2uXrHCN7WaxgMFXTf3fX2Z9c4XpN7145qT40b4vb0NlmpCNMw5LigKwEiY74SjowsBj99R0TXL3D8l4gHB/m6B6cF2qjPu8/h5IUVVb6nIi8Bd/izm0VUUN36Vtr8GR0YZGhuhLsEZ37tPSSsspCEvBPPzl3F82kx11wTtb9JJvXUdm5j3DM2ufLdxvj7bzVCpL0YycljdKaCwc2nvc+1HNR72VqPDP/2PUy0O+GrRKF/rUghSPIayVz6vWyf3wRNhmz8JHk4d56SiMSbmVvvaX1ZYW2mheO44J09c4IKayb7lgsCoOZbm6sgd/Q3FA8GDHpY2t4isnNvWlVn8kkt6kB16mrvAgyDLszTKYEMmCU5KKOl4EJbZwOeJNcTzfcxWBWF6URXn+6W8GRDAwxQrw/mEW5vj7hNLwoNoUgPM+f2YDXeL8klOjCIh6QGRKQ+l4pIXNBxw9QsnJvY+4YEe2Cr8xFEFM+y9Q4mOjSUyzBcfw/Oc2BaXFGzYF1ke+UjZw2vYa6mgpmuLS2g0CZEhBNxwxyP8GZl5BTS/isLNxhpbmw3BVUsTPfTUriJ7SQUdUwvMN0XcbZxu4BRWwMex+Z320jcRD0uszfQyVHQD06uamJjoYGhpjZFjFBmts8wsz/Kl4A5htsqcOqfIVTktrj2upeFrE9UZ39ppClbyMz1VlMY5YqiggI61J163o4gND+KmhxvXAmJIyiohL+sJSUH22No74eTssqNrtq195oSjvT2eITE8eNVK05dJFraE0LfBw/9DRl4LLcOD2vH6aCnKSFMYjl0xwvibumaNtZUuyhdOc/lHwUNu+6brlYiV3lweuJtiaRdExLtd9sR/KC4pgIcOypLDue0dTPiTLOorI7BRUMA2+L8HHiSLE4wN9NDV0UHHYaW9lbpER2zUlbli4ElI9jvqWztoaz9om056hqaY+p6V1ubtQ0iNnR/voaO2kNykWwS4WmNlbomNWwC3kwsp71mSCkOL+ovIC3dDR82D+F4RE99oqB1wP/obZh0MHoTwotEvtLxN53GIA7pKapjZG6MmdwFNM39ulW/mxEgWGf1cTV1FFe86pjbI5daBmGuh41UgOr+cxOHRZ+oGdzf9/khcUhsZQTykvIrXlQKRq6Kq8iV5qUHYnP4PV6//kcbD9k7w4Xko4b438X7+gdXlKuItFLF1DedOZj2fmj7T1NzL6OISw5111L8rJq/4Nc2pdugrmuBxN5mUknJy0rPJSX1AWmkznfvFSnadFIloAdHUex47KaNr6IlPSgMj+xHWIeBBIlpDNDPM8Ng0M0urBzxodv2j3ZP/LY0Hn2yepD0hI9wJIx0LrELyqfw68403rGj8I5VZcUT4Xsd9i4xfjyMlxvcQ8DDCSFs6AVfOY+x6j3uPX+x0yrc67oUPifYzR+OUDJdcHvEo4+UB6yRw201w7PDBL29ICkN2OgHrLI218uGOKuqqzgSk19K6JEG0BR4EjQdVfUwcN0CJAEucHI3QvHyO86cFjQe7HY0HlwDuJBdTM7k/nG4HPChZRxD/8gODY4L420YZLAsnwNIcQ4e7pLaPMDK6uWyknZqcu/jqXubiH4GHw+w094EHARGIFseZqIrEx1gH24AnpH2cZn1pnLW6EBxVtbG/lUPhVrjiZn0Rthn48JKscHs0FTSwCXxG/ofBDWXg3XVKmJbMMf21jESDo8hp3uTuy2YEl829rzXGmwrJclfg199NCHvdQfvcOF1vEwnVOs8vmtG8aBmWipDt3W6F3qIoIiyUOafsRXLvMmM/fAP8e8DDpEjM+uoskpmX3DVVwdItjgc1c0j+FHhYoacgGD8Tec6dvYi8lgnGutroGZhh5RrMvbS3NE4I6STT9DbV09zaQ9/0GsII7XKh26a4ZAm109PM9H3ibWk9nWNzbIvpC6kgS53UF+eSEReMn6M+V87JoXk9ioj4ZJ48eURCpD9+NqqcPmtK6MsWWrfCukVLSKbeSnO0VU+dQcHMj3vv5/aq7EsWWZrpoDInn7c1zZQ/C+euizGXLB/xfnaFBfEcn7PDCLI2QNEjky+NmSRFhuF/U1DZ98LHwwTVM6dQ1LPH1sNfmmLkFxCMX2ojnaMLGx2G3eDhUDvNfeBBqI6LvXSUxhNkrIaSSyYl7ePMr/fT9uY+3koXUA4R0hE20tr+O+ABRF9f8OSmAYpyOmgGvaVn7hDl+K3KLRa0jepItzvP1YuGOMcUUycFz4Lw6BcKg00xVlBF0+UBr0bW2NaX29p+17t4tpH3z2+guws8rI984lPePTxtzTHV10JHRxtl+fPIXlLismuadPRwdlWMZH2asa56Xqc945VgnbywNZIiYX1hjL4nFhhrOeHx8A3Ne6JMxSwO99Lf/pnPI9MsNd3Hx8AAu/8L8KCuu2mn+YQnT/aVx/Ek3L+BmcxRNL8Rl9w4iMLzdG15ntmxrwyVBuKirY+JcxjRBdW8K6mgszQABwNLrDwjeVz+mtKCB3ipnMEu4s+DB4l4jdWJFt7Gu+BsaICuazwvvq7tubYEh7Dx+gS8NC6i4xRFXEnX4aNNAnhoz+RR6HUcNtXVLcxMMLh6ipNHT3NWSBmRqqoLyuo2eCWU8bZ9hvn/GnhQRcfAA+/kd5sjokIb7B158X54G6n8gMaDcA7WWJ3vozhAl2thqaRXNdBRncjNy2cwDi0gs/QDn5ra6egZYXJlkYH6l7wur+RNfjIZIdbIyVpyr6CUZ3kl5KSlkZmZTWZNP7MrmwBz17WyPSlZltodlgWpIytrRkBGA80zO62EjfUOiXgQr7CyOMXIwBgzS6I9os3b33/QxN8AHiRLHbSW3Mbq1FnMYz9Q13eQ69APgof1VqrSg7impYuBfzoZLwr3ta0KKX55F0/9KygrmWAalMarrXbZ1ntRLi9zIrimeRVLv2c8b5javJevIR7LIcb+O3aa+8CD0HZZm+ljsPQWroLt950c8lpnWZ/7ymy5H8YX1XGNe8PbIcGeep612fdkJzyntK6b7o/FlN+x5Pfj9sQ1D9Mx0M/oUBPvq9MJUfiJ02cvcumKMmrqaihflUf22P/jp2Pnkb2sJJ2nqqSAwtnf+eWsE7cLPyFkooqFAau6GDy1L3BR5jIKanqYmRujp6uPiZUznvfzeFXZQH9TCemPk3ksHVx9RFyIO+66F/npN3XsbkcT+Whz0DU1k9SCT/TNLu+kPe4HDx8mme6uoMjrCle1fbmbkszj+Js46ZtgeLeKpi81ZITZYmtkhJaTH+HWcihbRJBY/Iqcx4F4aKqjEtFE9+SGxbRkoYeecsHl55jUDVBdzxATMxMMDU0xs/fEN76E6i9DfH77mGizk/znvDZmdk64uLnhtl1ccXMxRP3SWVTNAri91cfbqvOiQfo/peCrIIOh+32iU/L21aNiigsfcM/LBPWzl5B3e0xKVsG+dYqk64Q6GmNlF0hIoSBav/+1L+Iht51VyTrilRFan13H1dgSm4BnlA7vaoz+IXgQiNckXz7U86Ghlb7JEVbao3FRvPLfAw9CamtfBa+fPSAiLIyww0poCP42yijKXERWxRSH4O+sG3ab+JwaansW98FT4ZiJEa8vszg1RH/XZz5WF5OfGsXt69YYqquiY+qMZ0QaudVdDG6H3oCot4AXt13RUr9BYr+IyT/Urdp/fv47nw8ADxJY6edD9h38zVSQv6yHfsAL3tU/4Y7Tlp2mAB4EeNDHuwQPPCysMb9dQufCOiubOeKSvwgexsrjiXNS4vjx05yTk+OinBxy0nKRixfOcfroMa56v6DoR+w0Rf28jXXlup0LzimNLEvBwxW0ryqjpGchVQS2to8h/8s4wyurLE99oePtQ3xVTyCnH0r0g3AehNmiftUQi6hyar9OM7e2v0O6eSIEO5XZPsbLfLG+ehFt9ySS32/eoHefq0PAg2hhkrnaFFIz31LVOSa14Ny92aHT/y3wcDOP7OJS6t/kkfy8ht5lNvzZD/3Hexcsf3jK0xsHRDyIp5keqOR5oDfxeU00D+0fgRDaKJ+pyfDFSlYJtah22kc3bqh7/sN6D40vk3n8YHOEa3d7QjzDRFcRsdq/omgdw5N3vQgDxDvg4TKXNU2wdPPAw2OzuJtJR4svnL3EZUNn3K9tzr8eSlRqKe8nD494uKLrhHvIA55lZJCxWZ7ddcRCXQ1FbUduJj/n+fayZO4HOmGl8gMRDz8MHoQjI3RmRql9FEZcYiZZH0ZZXRxjtcIXG2VdHMLzKBnauTmL1xYZF1IAQq1Ql1dF0/sF5T3T3wn5XWdhtJU33rIoKVji9eQdTfsVcEUzdFekcFf/LL/K+vC4sZ8h0SrDH/JIcbjEr2ecianu5ut+qLA+Tn2KLx6aSlwyvk/Z7Np39mNPLfhjV4v9qx/4eW+qRWRuHf1Tkwz1f2WlPZYb2qpY3UzlWavgAfij4pJChVylLd0HfxttVPWtsL8Ryp3EFxR/+MrgzOZInXgJ8Uw5CU5GuPg8IOn9FILg5G7wUNnTSHOKM5fPW3C78DNtc/sfzyLmWl6QFWqLorI7cU2zDC2JEa9O0VebwSNnNWStknnVOrojziu4fYjGKAs3w/yqCpouCRROinZFM+0/UAfYaa5+pTLJCw9Tc4zCyxktuYmdmgIX5VTQMDbDzGx3MUFfUwmFC+c45pRDVZdgfyw8NnbEJQ+30/wWPAjPm5WRFtpfhOEemMWb9nGmV3toeX0Pj8uyqN+p41P/Rjb3fws8sNLCmwfXsLokz1ntMHK7ZxgXOvb7D5X0swADvzJYH4vLxaNc0PQjIm8L1m3Uja5MH7x0LyKr7oRXfj9jgsPBwV/GQeBBNFhD3bMQLM3c8Yl4zPPSMtIiPbluboDG7VpGN5XIJXOtfMoLw/6yHGreuZS0CZBG+Ed/BB52fpgQurv2+f8OPAgaD273X/J+fH9u+zTTkz30t2UQpHqQxoOY1YVpJge/0PmpmvKXaaTdMkNHToZzZ84he1WHq2o+ZGXfwNTACbeILN6O9DHYWcA93fM43/2T4EHI650f5ku+N85al1E09ic4t0uqK7Dn1ErX66M+2hhjFR0sAlIp6J6XPmP3rLdZl4S0qtXlJRYXN1KVZicGaUuywETNGqd7edTMbKY4LSyytCrkAYtYFMBDuA4XrvoQV95Bv0jEVGclBZ6XOKsSQFJlFyMbF6HUiq0t3ghjTXvc4ktpFnLtN+00I2wF2/IznJHZanttvMvKnOWsjBIyhj9ip7nI8lQjKdYXMfdK5FF5PW3VD/GW/ZVLyoboGltiZeODf1Q+75fELK0uszBcT8mDG9hrqHPR8gGvX4bgYaGDjrkvIVmf6J9f+47osBDV9ZWOsjhuXDnOOcP7PK/r4xvucFjEw+oQw+1vSIvNoOLLnFSTYOdq+M7U3wAeRKO11KV4oHhcBa/8PlqlUWv79+EHwYNogPaKTFKi4kioGGd6aX8k0jqslPHIXQ8TI29u5m6ISO+pk5IF1hY+UhAdysPnlVR9md+4B0r+CngQbkOriJb6qfj/2XsPryiWdQ/7n/q+dcM5Z7DehwsAACAASURBVJ/tNu29zRFFchAkKAqKGROgqIiKAiJgwISJoKKioghIzjlnZoCBYYaByc+3eggSBsUt53733FWzVq3p7ump7n66urrqV2+9740zRN9L503NADplG8p3J3Be7cCRhCxyLKMc0lkY0Oul+f9mdI3vpwkPcjol0dQ0QHdlMlfsl2PjG0b4zUekvkjhYXwEJ23/xlLbQ5yOvEvyizSe34sl9qAN621OEPW+mjZJeBjuRJl1ngBXJ5xcfdl79BwXbyTx/FMVzf0jjGiNlmPPpG9kqOIFaaed+fvSQ9yplzNjXHXmzsx0LvmMpC81NHyKJnDNb9iHvuZDnRxZVTppF3az1u48j56GEujlgseBa8R9qkH2/gw+m7az99J1Qi6c5uhs4UFdT9Ona+zbZIO7pz8HT0dw/d5LMoqaLeExDZZmoJ62z/eI2beVX/alUNGjmhWlzGQpBw9O7GSvrxXhwaRgoC2LJ+fOEJdeR53Mihimr+DzgxP4rrfDIbaJVoVulnWS1GdspjDtNom3npFcaqV/xGzhoZaxsX6UTWlc3rUJB69zhKdWW6JCTWFeiPAwtbPUBJGjq4/m0KIKDyb0Zbe4dsiZ9b//zu/zppWs+PVv/Pd//pfFCfeSld/adxVOpx7wqGgaJ8mPyJgGtbIPWWsFxem3iD7txy7bdaz5/U9W23iy++Jj3lTJ6bNiKWFoTCP5/AFs7U5zX2ZicMbAw1dIkpiuHxtBPaxkSDmMSvJ7Z5iv7fP1fwtdmis8mE2YBzJ5EOLPnl3HOPWwkNYhDSO9adw8NF14kA5hRFWfxpMLe7Df5s3+pCYkz/tSOf9rwoMJdfMXclPiLXOprly5wux0+fIV4l5XU9erQcKg7WugKdaOLWv2EvKsgOopkJKAUkRymDc7t+zELSyFVvUXon024Wbvjvv+M4SEhhN6PomsThktTfl8vB3MYacN/LrCjTPPyqiQPC4XJ/PgpCN//raOnRfSeVc38HVO9DTK0rwaeXUaMW6/8edqL07ez6F8aHaHQaoolfQ1vCZq+3+yYd9tnuV3oZdUYEUrvQ+8cXULJjK9nDlW8tOONWNxEYWH9HI5Y9oxNKM6S4N4xstnxkHnrswrPEjKnFHLqGqYkTE9emst7YUID9ILaEyDxtLQ+tqhls7ErGqk9eMl3H75J56XP5HZOD66OiU8TDiXzJntXDLEmz3uBwlK7mBgaNLx5IRzyZmHAL5aPKxfvZo1m21xdHLCaSI52qzi99+W8OvSVWyw/7rdycmO7ZtWs/rP9awK/s5Uix8SHqQrN6HTqBgZkbzWmjCNyNBmBuG11ZWA6+l87Ju8CBPDNc8sPh2c7bzYdSGdMrlmfhHNcnvNGFTdyNP247l5M25nHvKsZmLUeuL2m5Rl5D06yc4/V7E8IIXclkGk7vVYRz7FcV6s/Psa/OK/kNupndFZM/Vn8uTcLhw2u+F4PotenXHhFj7fC6c5cW7f/pouPJzj3KVwHtyN4kh4Gp3ph/GycWd/5Cs+SfwWLDxIRzSjUykYUvTTJzkDG5acQI4ypjditAiy41Ndht6fYu8ON/wjUnnXbcA8Q3j4QKGyG3nFXY6tW8bOs6m8rBycOTJgaKfgYTBHHbawyiOCJ6UyZOoR5BWpPDnni+0Wb069bqd5UKpZxj9SRAxDewpXfTaz9u+/8LvDEYLf9qCeT0idDKd5+KvFg3rwM8kXpDBcgQQ9b2IwMwR/Ox8OXH5GRqeCwUFpmsBkklGT9Yhonz9ZcWgxhAfJz5je4oRNqRpFK3nbNjRRmRFB4OrVOF4rp7Jr3BJv0YQH8xjdOXeID9jAP3/biNPFj3xpU1qt/zGp6KtO59mRLaz4+0rcLr3mbb1qWrk2o+t4w+Oznmxbvpo/XM9xt7jPMu/SWlm1Jjxg1Fmi9gwNKhlWaxgd7aEw6RxnfGYKD1LDXtWWR06UJ5v+sCXgxkey26RRk38f4cFz60ZWb3LAbfe4afOeCVPm8W9vfDx3sP6ff8NmRlSL8SgPX24d56jjan5fvoylvy2xRFv57//8jVW2vuyPeEBSej4tmWfZ77yNdRu24+jtyS4PezYu+Qfu4T/m48Go7kJeEMsx2xWs236IM/dyqRu2PiovhdLVdr7mZqAj9ls98LmQRonSNI/APx6dZjxKjgmDRkHbvd142e3l0I0MSnXTf5dKkAlNZwnF191YbxtKQk4jXT8qPJj0mBSlZD2/w80rl7k8p/11mSvX4ol+kEvjsHbcr9BQKaXPT+Kw5E/8H7VTJZ9o85gUjMhecHH7CrY6BXPp2Ru+5Nzi1Jp/sMX9MAHHggk5G0Xso49UjAzTKYnVod7stLHBZtdZbhb0oRjuoiLlIqHe29m0Yx+B9yppHdZZF0vNo8hLnvMoyI7lv25k950ySntGZ9ablgfNusWDWV1D9dvL+K525OybLuqlyfYL+Sy68GBmtCGDDxG+LF0RQGxFH51fA8pMO6MFCg8YMWhH0ailTvR4FK1pmYwPYHxPeJBqDrOeMbWKkVGdxfmzJY+/KjxIbyWzEe3I8Pi0UqntMtjC0KtD7FjlzNG72eQNfOU/6WfVqvBgHkHZU0nW3SjuppdR3a5AMyo5l/ww5VzyseRccnQUVV8H7dmPSLyfwZdGmcWfguTQ2jQ6yED/AAMDgwwpVaikNqZWh0GvRC7rp18xKyQjPyM8JHHj/jOSzzixdLkHFz+00qA0YhyT0d9eQMa79yQe3I6jQwBHYjOpG9GgH64h7aQDvoEHcfPy4cAs4QGz1EkcZkA+gGJwCKXU7pCm6+qmC9s/KTxYRtgn2/GGcX9NMwsSfFd4kP5gQDc6gmaizTo7C6lPOcPHQ3Ie3fXp3PFdy/rVbhxJyORL7yxB43+D8CC1/vQjqJUK+qdZQk9aRE99y3upTzpCgMNOXI/Gk9r+1Wp6ap+p//ejGNag0U17FpRNlGckEnNqNx42q1j222+s3OiC55EIop9mUdAoZ0g13uac9BM/nbG+6gmPg/3Z7HiRVDXMFzBD11dD2ZtbXDl5GP/AU4Q/yLH4a5s99jg97x9Znis8SJWCrof64nzyvpRTLxtBL4XlmB5Oc5oZjmlURnNWIrEHnVhtG0RcdgdtKtNfFB7AOKpE2ddFe3v7vKlrYFyJlBpS3xIejK1PuHHQlc3L17LF3Zfjt25yyHYNvkejuP6qhqamFpqae5A3pPNIirXt4IiD13FCpDmYnSrUOgM6VS8dxS9IOmmHvZ0/oQ/yKJ1WKUqwJU/gHUXJJJ5wY92S5TiHPOdlpZw5A5XSzvMID7r+JlrjHLB1CeXa60qkKW4L+iyi8PC6apqytqCDf91pfuHh6z7zLi1IeJjv30aGGz+ReXkny5bs5OLbeuomnqavwsNPOJecOuxX4WGLx2ku3H5FvsVrexFFRUXkPTrNYVcX7Hed4dqHwgmP7kUUFX4gOS6Yg3bfs3jwwGm9Pa4nYrgRO+6QKzbiKId32bFhw2RUi8lu5NRJzVgwqXoZfX2QnZs9ORibwRel5KVeha7lJbdOuOO4cTNbnAIIup7IkxevJpz6jTuyfGVx8PeK9A8l1HQqLBYjUmdV35NB3H47nB198Lv4hIyKdnoVCnpbS/j8KIzTXras3ejNidcdtE6IjqbRbnqK7xG8bRk2rkc5cyuD3Pou5P299DZ+4VX0AfbYbWOH9zmuf+lnzDjbumTGZc1aWcSpFmdWsc3Gho2btuLuG4jvpWe8Cd2Crd0hzibl0yg53vkh4UGqDCY6BNKiUctYXxNV2S94eus+j56/Ibcln5TDG9jhdoaI5DI6x8xzhIei0TE08go+htjg5BFCRGo57VOCqqT3KmgrSOHhxYPs9nDH3WM3+48dJcDHnV2eey1OuyrkY2gmo3mYR9H0lfPhojvujr547XTFf7cHO/ZG86J5BIVVB0NzLR4GKu4Sc8gHtz2XuFshQ/UpFH+7PRyNecOXOeGltHSVvuD23lWsnEd42LrDE5+jF4i5eZObsTHcvHEWf7vVbPeyZvEwqxhIq/o6yl9fYN8fa9kZ30h1r86y06IJD5KQpKilLC2CoB0rWb7WGe+T0cSnZlNQ3UJHTy+y7jYay3P48DiKSwfdsVu7lg17o3mY12YJdTv9rM3aflqzE4k97MCqZavY7H7I4ivm+Yciyurb6OiRIZN109XWQM2XJO5f8GLLvM4lpXfJZFSLWcKDNBVrbID+2tfc2b8N972XiX5Ti8z4byQ82Dpg63WY4GvTIrxMRnq5fonLFw7guvIX7GZMtZCe1xHq3z/gUUwEl6Nvk/joKa/iA/Hc6oRX0E0eVfQgk8tQ5V7gsKsbTtIxblwn6upJvNf+hs/lN9x7nMLrWxfwj8mhtzhmnnCaZktIvLr3cVzcbcOa9bs4Ef+e3Fal1VB74wXThFmvoOVTPFcls+YdvvhHZ1Gl0M7/n/E/WqJatN+XhId9HIp9R5mlPpCcdI8wJOtjYFDFsEaJsq2cwpJGOgZHGDP/oMWD1MHUqxiU99A1X/uro5vOXiWaiTB75nmEB/NIB4OFl9m9ZjXrV2/F9dAxjp49SeD6FXhe/cirL/U0NbXT1lRDV9k9wvY442znhk9QFAnvauhUGzGY9Gj6Gih9cZ2I/W5scjjN7eJ+uiVzxqnPeB0tL0vhYagXzlu2sW5PPK/rFZZwjVO7TS3MIzwoyylPPYfLUkfCPnTTOF+LfCqfiYVFFx4MyAse8+CYE0tsr5DeNsSg1bbgQoWH2Sc8e30BFg+z/zK5PiU8OOC4xQ3PU5MO229yI/wgAa622MyZajH555nfJkWTJQyxzZ8enEzKs4Qzn7kH1i0eLMKKmqGeTnoGRtDoJFg6BpqySdrzC3/uiSelqNPiZ8ps0KFTyujtVaDUTBOwLO/s8aOZdMMoOysoTE/k5rnDHLrwgEdZLbOsA35CeDhxmeOh5znj6sDWk+nkd6gYkayNzDrLdOGqJ6fYs2Uzu6RQ28UyRqWBYKMGeXEG2V9eEX/1BEdmCw+WU5faHV+JGbXDDHVVU5KZwuP426R9qeJtauJft3j4mvX8SwsSHub/uySe6tU9dOfdJtTlD9Zt8SYgMpmssnzSQ/cSlvCGD7V9KGdPD/hfIjx868qmfjMb6E8/zVFXH3adSeKT5GV6wR8zJmU+L6NPccjTC99DZ7mYkMLLrCJK69ro7BtmZJpIMTdbM+rCu9w5sZfNntF80oKVSOpIImxWYjhhhw9xKCyS2KtnOLTbjzNxr3nXOFuEm3uUhWyxIjxIfzOitTj8mlSWTJjmER6kwjLWV0tVRgKnD10iuazXMqdk0uLB8Yd8PCzklKfvY2ZMXk99lC1b1u4l5HkBNRMvZMxautODObF7N26e+wk7v5+dbk5sXb4ExyPxJGT3MTyqtzi6NPbl8+7hTa5fiefe6wKq5Npp8zOl0YY++kqfcvtaAslZNTRPzpuWrr2/gfJ3d4kJ8sJ18ybW+UZwN7edjnlGPcaFhzdE2/4H6/bE8TivAx1GNLJqis+vx2bXFRIyG2aaEk2/5NnLiyI8/L9s2nmG4KgHJKekkPIX0tPIoxx1tWfV7KgWs8/X2vrPCA+GPpo+3eKqx2Z+c73Jq9q+qRf1V+FhJ37hiTwvqaCiYiKVJHHtkCs77X3xj/tCafm036rqqWuRozKZp42YfBUetu69zu2Mavotoa/GTYH7c69zfo8vHgeieNI6yODkb4MtlLyK5qzL93w87MRu5Vo2ugcQYInTvZ/9e1xwstnA6gUJDyZ0g2203XLDYdNughKzKBuTPKrLGM48xZ6ty/nH335j6eqtOLi74z5P8tgfzcPP9fRK72+zNJ2jn7r0KCICPXF19sD70GnOXrjA2VMH8HN3xMnFj4CIVPJlOtSTLwTzGGOKOorvn+KopxNuHnvYdzyYc+fPcjZoL552tjh7n+Dc/Wyqh6yNwlgrJJPbFkF4MI+h6iklxX85G1csYfnmXewNjSLx6W3C7JZiF3CTxOz28elOCxUeJE/0ukF6m6soy8vk3cunJN29wfXzJzi6fy9+fqcJCY8iKSUcn7Wr8QxL4WXVwLhfHMni4fW4j4fD8R8oksIeWZxBhnDA2ZODV1J53yXFApr4mHVo+pup+fSIhNNe7FjxD5atWMlvS5byxzp7HE8/4GVeI+2Do4zpRlB2FJHz8Bx7t27BN/Q+91Of8+JWMPvsnfGNSONdTR+Ksa+q+vhRDHR9iif2kGTxcJtCpYKyJycJ8vXDK+QphX0KNJ8l4cEL/wsPSKtrp6OjY1pqpujNHa56/cnyeYSHzRu3YuPiM1HeA9i/3wv7NcvY4LEw4UHyp/Pl4TFcft/M3ifd1EvxaaViO9qGrPgqPkt/xrnkBGvTKMPd5eQnhXPaxx7HHQ44efjhf+QUZ0LPci70NCcO7sXX3Rkn513sOnaN2x8baRscm2btMJGXRchopv7TfWJO+uBqs4Gttk64+gQQePw0waHnOHc2lODTxzm6fxceDptZs84ej+giqrtmBgW05Div8CD9arT4fGn/lMjDpAw+lHcxZBoXHtrn9fEweZ7S6M2sqRYtWkzaYQb7++j+cJWLfl7Y7rrAgwYZHb1y5N2ZPL20F5/tHvjeqqKxoxeZXI7ckvro6x+yOLea1ML4jnPJXY572HfhAa+raqmtnZVqCijOvsWJzUtxPTnbuaSR4d5W2psaaGqXIe/rQ10SSdCEc8n3/aZxMTbvIkfd97H/XCIvasqpLHzKBfvfLeFmn3+uo7WykMySVlQVsYTs2k/QlVQ+W/x1SE+hnuGOEnKfXuFcgBtbNjnjd+kFmdL0n8k68CvKOUuGoUbKXkYStteRdVv3cTrhDVn11p5Bi3ppcdQ43NNIaYwn7ltdcD98ibiXaSQn3eXWjatEnL/F4w8VNI8X/2nHk4SHHNKPr1nYVItp/1zooklRTMmTIOyWrMY/adLiwcRoTzkVCd5s2ezHoWOBHDu+G+ftW9i8YiXuUYV8bhi2dBKN2gHUza+5dfEiUfFPeJnfQOvQV0stqRxrZDWUv3tI5KW7fGhSMjAhlErCrn643VIPxp/2ZKedHdu8zxP1vhWZxjDt3T39aowMlj7naZAzv64K4n6zwvKuM/TmkX/vBFuX+xBVLKdtoUN7PyE8mHVqVIpeOtvbaGubTHVkPwon1GMrSz1ieVVcQ/3Ub5P7SN8tlLyI4frebfxz42nu5lVS0dpGW3sX3bJBi1+g2TX6dApflxdDeLDH9s+NbNkp1eETzlB3O2K3aQMbFyQ8mBjrqaQ22pkNq/w4l1ZMjRUx3KrFw9cLmbZkXXiYtsPEohlpCqpa3kxDWS5Zb5J5cieKyLAgDvnvw9//ECdupJFe0oF6IkKPxvKtpqfwOc9OOPL33wKJK2+jSTUewWf8d81UFCfLO3u6j4eTMYTdeELStWhu5vczMDX1xYS2v4HGh354+YRy/UU5DZOxRiVBUDPMiLqOLMlfxyzhQXJKrO5rpaGikLxPb3mdksT9uGtcPhvEoYB9+AcEE/u6mBcpicT4beTvnvG8zq+gvrmZ5qnUSHPdYyL9HfHxsTLVYi68uVt+SniQfH+00ZJzj6v7nS0+NhzsXPD2P8HpmCc8f5LMJ2lKysjsqUJSHamks/wtKTcucyG1kd7vOKD9a1MtsngbE4DLRlcCX4/RM2egZS4Oq1t+SniQHMu3U53zjvTkdDK+VFHfo2ZEshiyerBZG81jtL25QsR+b2wOPLRMr7MWTdPQkETM6ePsPx7Nk7wK6iuyST23C/+TUVxJb7Zu8TnrUN9bnUd4mP23bwkP0o0fQzPUSUXmJ8obWmju7KazNpNPiYfZ8l/L2X+vhmLJWczUZz7nklM7fGPBxNhgL/LuDtrbmqgteEPK0TWsWR/IhbQSGqWXr0mLSdPEm7Ou+PiFEnLnLVXlL4g/7IrLht8tpn6HIh7x7NU7MnO+kJf/ibev0nn9+gO5JWWUlZdTLnVQKyuprJQ6pGWUl+Xx/s0HckoaaR8YQa9ToWwtJOtJBGd87bDdaMMOvzAiMzvoVk0KNlYuw6xG0fyRRPe/sdYpiLDbr8gtzif7zX2ueyxhy4G7PC3oYszKX61uWhTh4f/hz7U72OHqzR4/P/z+QtrtspWtqzfyx/+w8GDoL+DT7eN4btyB040K6vq+mldOCQ/rNrPVzY9AqWF/biKFHsDbbiOb1tlgu+cMZ89O+y08lpiHubTozJaO4Tj3SeHhb2xyCuDw+ViSnjzhyURKuuKPt+12NjsGcOrOYx5P/XabqLP78ZEEhG86l/TAeaMjO88kcOv2HUtYxNtRwZwO8MTe4QCXi0bpmTHSI5kVD9PX3kxzfR211WUUZT6zjDBvsg3iUlopbUYzkid8VW4MEWcOsW8B93VfUALP8ppmOJI0qZqpyLhD9Gl/fNydcXRwwNHZnV37TnI27iUfGpSWubczyqdRi2mwis+PrnD+8G52uTrh6OCIo8tOfALDuPZMipgyc+rGjP/Pu7IYwoMaZWcOd3atwtF+J/6XHvHk7WtyHx3Dbvk6/GOzJkzTx+s29EUknfDiWHACt78MYjJrwVDN0+MO+Ow9x4XUFsv8aNNgMW9vRxB2ZB9+Xu64Otlj6+DGLv8ThFy7y+34KBJO2LFywwEi31ZR3i5D1tFKS00xVQneOG7czZFbmZRIprVGDSbFB+ICnNh7LIbYnH70JiMGdR89rXVUF2Xx/vktos/sx8fZAXff/fj778Xfxx1HFy92H7/K3cwyikpy+HT/PMfdtrF55zluZ7fQPjxMX+07UkI82LZ1J0evp/K2osfSCJKmRall7XS1N5D7MIzQ3a6s3XOLku5C7p1yw9s7iKD7pQxoFIxlh+Jva4edRyBBkTHExExP17l4MgBfm99ZGmh9qsVWO2/2nLhMwh2pvN/izu3LHPPYjvv+S0Skt87quJvRjw6j6Kinvr6OupoKil/f5MbxnWxY58X5z4O0TzQGzDo5Q00viD4ayNlbn8huGJr/xawfwdiaRuKl05yPfcGr6pGvAs9kGTTr0Ks6qXt/n1sXj3HYzxN3Fycc7O2xd3TC2c0L38AznItNJr2sB6XONE+nR8rQjEGKTlH7gdS4MEIO++Hr4YqzowP29nbY2zvg6OSCq4cPuwOOcfLSTe5ld9CrHLfmmDwly/c3hQfpUCbQqVAp+ujp6qK9rY2WuhI+RzjhYLufoDtZE0L9jFwtK7OFh9T6fjQdX0hPuk/iuT14b9/K6s3eHLlxh4Q7d7l7O4zjPrZsXbUJm8DrxMbf4e7duxPpIQ+fZFDQPcbw5GXMEB4Sufu2nK6GYnIyX5MS6oDtlp14n75B0udcS8hmKWzzVMp5y/sXkQRuWMJ2n3OE3cmgoLyeZoVpTszz2VEtZggPOw8TFPWCLwPttFWnEG7/Bwci35NerUKrHkTe1UrXp4scdtnH0asvyFGZscx97avg871gju2yY8t2L/ace8j7pmEGrXSW5pKVtpjRyispTQnnsPNm1m/14khkKu+qehnSGzDqB+mpr6CiKJ+87I9kvkkj+f5NIv1tsFm7mc12O/EJ8GP3LjecHRxxcj/D1ef51OkNFj8vvS3NtEihYxtrKXr3kBs+v7PU9gL38lqQovJayuDoAHN8PFg/2ZlbjTqM6j66OzvoaG+lqfA5zyN82fDbZo6kdlIrWYMahugpS+P2nnVs2HuXlKxPZL2I5pzPdjav/oOt+65w6VYKLzI+kpWbQ96XTF48fcH7rFzyS6X2l9TmktpelVRK7bDyYgrycnib/pHCBhkKyZeJVslQewm5KTGc993Kts022O4+z+XkMrp0MCVwzTx7qXHIcNVLXoS6smSpB8HPMskoLCHvZRxxJ3z4c8MZnjQNIp8j4szJaHzDTwgPpoEaKj4+JSF6ulVPOKf8XbFfs5J/7jhEaPhVrk9a+sz4vs7F47vx3fYH/73Chf3nLnHpehRR0VL0uFyqR8zzTOGZfR2LITw44GTjgXfoZDjnO9y+fpJjuz1w3XWamAoDQ9OfDbPB4gBX1tJIk9R2qSrhS3oiN3xXsWpbMLEf66xa/C6+8GBkdLCd2lfXuXb2CAf9duHp7oarpx/+pyKJS86msLqSyrIvfHj5ghcvJlMaSdEhBHts4L/+4czhm/dJTJ38Tfp+ycvsOtr6R8bfYdOFhzNPufexk77WFjp1TJs2ZEav6qH3y23upJRQ0T6MVFWaDFrGFJ0Wcaq1PpOUqMPs2WaHXVQtbQppYuu41Xf9h9tcCj7O0QN72ePljpuLC85S5InDZwmPSyWzuo38t/eI8V7Jf23w43joBSJmTKW6zJWIQLxs1uDg/T8vPEiDNl1Fz3hwxp1Va3Zy6Op9EmIjiAjyx8s7kJPXH5L08j2ZOfkUFJeO99EmBgsrK0soyHnP29TnPHuTS0FxCWVlpZRW1FLb0mex3p3eMf++8GBGrxlmsKeN1rY2i+V9S/Ej7obswnbjTg5n/JjwYNaqUQ/K6O7uorurjVIpqoWdJ7tCHpM1Eadh9lP5rXXJsmW6dYu1faVoXYOyLtpbW2ltbaWtrZWmmtxxp6Wevrhf+oQ0k3huNWdGV3yd88fPcOhKOlVqPVLUlJakgxw+fokzD8utWklYO4dvbVu48ND/koTDLnjNo4aZNIOoK1J5nHiL+Ph4bkSc4pTvNpb+upWglEYqx994E+cyTXjw2ItvXJnlIZsa0Zv3jCXierpyn5IsKZMRYZw9thuH3//BSoeLJHxqsMA06xToO5I467qdPWce8KhEidGgQd9XzJuYYwS6bGHd78tZvnw5y/5Yz0abHdg5OOHs4oqbuwceu7zw8vbB1+JR2gdvby927fLEY+dOvM/c5X5WM8P99VTEueK8bgXL/rDF7fhNHhbLF3AdBtS9lXwO2YjtmpX8scGGbfb2OGxbbwmV5X71E5+bVPM385w/hgAAIABJREFUlGezsQgPDzm58j+xO5nKq4oJJ26z97O2buylt+YBZ/78G1tcAgk8E0HUjJfb9Bfit5cjT/vh5+jAeodQHvZI3lKtHXCebX/Z4sGEqvwJSWe82LLjMDHVWuTTRirGhYfdOK39g2V/rGPDdltsbReQnAPYG/KcMq3pa0gkycdDd7ElnOa65b/wy69LLeVHKkPfTsv47ddf+HXFelbNIzyYBz/wJGw/B/ZdIKpAh3GiljQNVlPxNpEr52J5Xq+d6fDKLHn/rOBt3CXCg45w0N8bL/t1rPh1KZsO3ud5Yc+iKJNf75gR3cgAsqYKivLyKKpoor1fjWZ6jf5152lLRqSwtu21pRQWFFFc1U6vSvtNj/7T/mxlURIeLhHmbc/G/Y+oGjH8+HWaR1HLK0kL3k/U81wK2tpo+vKAaI+1LNl8klt5rZYpEJaDm/WWiAS5j27w8OkHMhvUc4WHtBYwqDD2PufqPnfcdrixKzCEC7GPSclpoG1Q8kkgpzX/ETF7XXG5mE1JWxv1Ba9JiY0g/PwZgtxWs2b9PoIf5SL5tJRG+jANUhB/hJDQSC6/akSjG2GkPp2kq8cIcNnKptWrWbXFA7/w52TUyOkblNNT+ZbnVw7iuWUdO0Ouc/rYQY5II+eOR7mS2UGHctxyQjL7H6x9QYzfJrZudGH/tVd86hxDNyKnJjWK21FnOerriP12F7YdfUBdRTynPezxPhbP3SKlxfRbmx1KgO1qfvtlCb/NcZK0kuVLf+XX31bOcS450ldByuH1uB+N52F2+4QZq2Rd00V+UiRxCU95WiCb1Xk3oGwvJ/fWKU4GHePIfi/cbdezZtU2NnlHk9GjsR6dxUoJ+qlNJilsVQctVcXkS53hglIqmnqQD1uzcPj+kYyaAWRtNVQUfSEn+zPZOXkUlFZR1y6nXz23WTAjxwnhIcTX3eJcsm/CueSMfaRZtZ355Kbd5drVK1yJOMsRxz9Yu+2IZTpR6zyHGBcebnDa3Z19oY9IrWhE/vkCPptWsWrZMpb9SFq+hjU2AUQWKuiYFE8nhIcbLitxP36XmJsJpF5wZ93qVaxauYylS5exfOUf/Ll6NautpFWr/mDFb0tYtnwlv/+5Hge/UCLzdKhnObKdEh527iYw/DkzhAePwwRdus2rknSexJ3Ce906DsRk8b6ijZ7qTyRL3uZD3XG02cvR2HcUSxZkkgnzp3Mcd9vKJvsDHIv/RI3StPAoCFM3x4xO2UHbpxhOua1lw0Zfjse9p2x4mNHBAp6e3o2f/SbWr17JimVLWbp8JStXbWSrvQde/sc4GRZJ9J3HPH/zmbzKFjoHR9AZlYz15/L0chgXg4MJPnOcw3vd2f7nCpbviiO1rGvC2ax53Llk/E48pLnkk84lp85t/gWzph913Svux0VzPTKcs4e92WWznmVr/YjM76N9xIzk3LQqPRzfNZvYc6uC0q4xDBoZXaWp3D3pgdOmVfy5YhnLVvzBH2s3YWMriW3OuLi64e7hiaeXN94+vuze7YuvjzfeXrvw9JTaZb74XvlAoeRLqK+M0iencV7xC0uXrcP+kGSl1kL/Atod+s5c8hL82fDrElau3Y6tgwM7tq5n3QZ7NgY+o6hvZNp7f34Wll/0tWTfO0XADmccoqvoUn5j0GlWVoamFzy/6MOmFd9rQ/zA7yttcfKPJrXP9DUa0qzjzlz9SeFhIJ3bx3w5ePQGt0onVUXJ52Mp+am3uHYlkRctxq/WkNLBpWgVqmLSIs8SdvwIgXs98bBdy4qlf7D5eDIZ1X0W/yEzz1OaavGBz1d8+edvAdysmnAuOXsny7pk8ZDDY79/stovgZTi8akWc3c1MNhWxKtj63FwdMPrsBRq8S0fK8fDi0vuyEy9H3kdvZ8dy5awZMkC09LlLPOKI7mwY7wcWYSHJIJtVrM7+DnPKr41rCg50pzsWJrRD/fSnXWLuOtXuBi8n72uW9m8dSe7H3XSpZQsg0yMNGTyKdKLVWuksNoHOHL2GjefvCOrssviPHDcrZqetuwH3PD8J//x9yUWC8mVsx0grvyNX//xC5t8wriS/Rd6xD9h8WAcKOJjwnG8ttix7dwnKmQjaI3D9JSn8yTEE5vfl7Ji2Qr+XL+VbY5u4300L298fKV6YtwP0G5fX3y8vfDa5YGHuytuPkEcu/7OEr1k+mthXHiI4dDWzew7n8bLhtkvQQOKlmJyH14h/NJlrkReITzIB+8dm1i3PYDzeTrkCw7/DnqZFN76Gbfi40hIiCbUdws2Gzzxu5RGoVX/LXNL6o9ukZ6/7JQ7REvRNS5dIuJyOKGH3HHctJ6tXiGEve22iA7W+tyGlqfcPHmIgKNR3C9qorcxh8dn3Nh7MoaojLZZ045+9MzG9/8B4eE1d0/sYq9/ONdy5z44ZnUvo3mXOephh82GDWzYsJHN291xPnyP982DDM64t1LIgSbeX9vP/l0B+MWXL6DDLp3wuPAge3eFiEM72SF1JO2ccfA4zKXUCqplE6PdUidA/pG75yJJeldKrSWciDRaoWNMLfmQ6KS9tpj8dyk8uRND9NXLRFy8wPmwUEKDT3PqRBDHjx3hyKFADkqhFw9I8bLH42afiHlJelkfek0f6lIpdEocd18WUtktmQ4uzOunNI9stO0TyTFnCT6yH/+AQAKDzhOe8Ja8tkGGpj8l37uvJhm9tY8JWbcE19CXvLVE+/jenyZ+NylQtGdy95AfYXc/k10rQ6VWo/4LabA6g4zbVwk6d59MhRHVjPv9nfP5CeFB8u/w/mEc56+9pEbyjj2tIywJD0PJe3HbuoejCa/53Cs5EFpAsjgGHLWo0V8fTB3D3aUk+y9lh98FriVlUlFdTfX3UlUeb++Hc9rVhs1heVR1q2d1qCS/lV00lWSTk11Cpdz4Vc20eJbVoFaNWKb+zHAWY+kQd/LlfjiXjvqxR6p8/Y9y9OIjy+i1TGPFJO07t+F7P5slXy8GPTqdDp00MmdamG8Gs8k47X9GjNKcyu8dbN7fddS/vMqlfe5sP/KM6r8iPEiva6MejXIQteStd6yX1tzHxB/049DjOmr7xqaNmElnakCnGUEzqkUrDaVZLB5qeH7Cid3+YVx80WJxloVBgbxbhqx3gEHJE7DkQEkreZyX5l+aMIwpGZa30yYfZVSnpunTfRKOu+Ps7IKzs4dlykpGlWxao1dygtlL34ACxZgBk16NoS2ZuNBTnDoTyc3kLAqb5SiGJ5wJSZz1Y2gkB0fNtbT1NpOdep8H0bHcLehhQDN+Lha0ZpNl1FDV/pGH4RHcSf5M0aARnUpG3aNjnNy3ExdXL/aeiiYhuw11fxGpUbEkvcqjXPLhMzaINvc8B133cvjqM962yadCy447Seqi8uMji2nnmmOvyG+edJBpRD82QMuXl7zPq7GYCn59ZI3oNOpxZ6mWubrTC4EU6rGWyqfnxkd1fHezJyCI4KgnvKzoszjJ/JrP9P8t9rL0HjFi0OvGnwOdHr3BmufzBR534pnS63RotVq0WunZ0mMwLmAKkmmA8uQIwvZ54RxZRP88woOxO5NXsSfY7WhvsajYscOFvRFpvKmUW+Y+WztTS4e9No6zXt4cPP+EV03D6AYaqSjIIy83h5ycH0i5eeQVVVlM6EcnDR9Nw4zIPxPvuQbv0w9IfJFNTXbKeLi6KUuJuyRawtdNhKqbsTxpTXGXu4n3efrqE/kdRmYXG8t1VMQQ7L2PI1dSyLRMtVCjL7jMSZ8gTkdE8uDVNQ67e+AdGMmjvFbaeqqpzogk0N4Wh+22Fidg9z83Tzij02EaLCTz5Ts+ZNfSqdJaRIe/Up9ZytHoIIMNGbx8mUV2eSdqkxadupmshPNcj7hMZNx9kl58IKu4lqYuOfIBaRqfkmGV2uK4dnRM+7UuNqkYVeSTdHI3/judxx0fu+zEffcJLr9uoHFgIrKOZHUzqqD+1i68nA9wLCGT6gW+r81jMoZr73POxwVX2+3Y7nDCbfcJTt3Jp2FQizRjy6xppyUvhStHr5LeMoRcas+Ypbpp1FI3KXpbqC/5TGbaI+7FRXE14hLhF84TdjaEkNOSsHicY0cPcSjwAIGW9td42yvwcBDBj8qo6R7BpJfRVfmG+JPB3HxZTHm7ApXWwIx3pLWCLbUiDcMoWvN4dzOE04f3E7BvPweDwrh05yWZ9apvONy1kqGhntxHoRx28cQ9robu4YULD4wpGOhqosZiVTttqufkdNC/8l1ZS31zj2Xgx5of77lX8DPCgwnG2qnN+0huXiW1/ZMPtyQu6C1OBNVqDWOGyY70xNHNWgyjTXyKC+bcofG2i9+BE5y48pyPDQoGxqy3o3VNH8m5to/lvx8iobpvPKrF3AuyTIVSWHwprWbb4Xukl3XPU8+ZLSF3+1vq6ZD10Tc07pRRqzdODfyZNd10VOfyLvUHpiCnppH6qZbWPvX4iLJRgb4vmYsu2zkY/pK0moX3No3DXQx9DOeotyv227ezw2UP/mfvk9GpnxJzTKPDqOXtNHYPWJxaK1VqRkbHsFzHVMVkoKsolQchPmwPek5eXRs9U04MJQeHcvq6M3gYFsipkze4VTi3j2cV9fSNPyM8DFaSl3yL8OAY0tpGLVaDkqgitWdGBrrpqPjI+xdPeHgnlpirFzkfcppTJ09w8sQJTgQFESSl48c5LqWJyHVBF+K58arGEmFoetvArO1D13iT4zu2cyDiJelNsys/I4rq97w+78qm9evZMNGftXEOIPDKKyrUZks9N/3Sv7Vs7Mziffwx3LZsZvPmzWzcaIPTwWji3zVaCTX/rZx+4DdNCW9jT3PIyQ47ewccHJ0tkYT2nrrB7TcVtFuJeDGVu2GA+oxYrgbsYN3KlfyxYhmr7Y8R/ryIKsX0aXBT//jhhQUKD5LDyT466yuoqm6m1ZrHX4MW42AzNaWFFOTlkZeXT0FROWX1vShGDdMa8hPnaB6hv7WGmopqqjqGpx7071+BZFrUTFN1KYWFhRQVl1JaWW8xa5Lmulg+kimXTkFXczsyhWruqKzZiEGnYWRogL6eCTOm1hZamptoamqksaGe+ro66qS5pTU11EylWhra5fQN6zAbdZhUkvlqN72K8Y7h9899Yg/LfHAl/Z0tNNfXUlNTS21DC2090jxYqWO24JwsnSCtuofGglzKGuX0qX6gYEjecEcVdNfX0NKjtMSv/pFDTz9L06gCRXcbDc09DOjNc+/39J1nL5uH6O+ooOC9FI98mOGxhXaaJbNlBQM9nbS091s6bDMqGMMY+t4qyoqqaegeZMo1x+zjL2hd6jgO01v9hZKacUcuY5aOgtRZ+FZSMyRro6m8mKKmQVTWrs2kZVStZFgpzdda6B2QGnNjDHY301xbRWVlFVU19dS3yRnUSGHHFnRR/4Y7mRiRt9JcXU5xbS8q43Q/HD92OZLHeMvHpLXEQ+6orbaEctV8F57UulbTW19KVXUTzTLJQ9BE41rqMM7X+pUcRUnhkCRnmmYjmoEuiyVISUkJJaUV1Lb1MTAys+FqNkqd2ok5fFK9NtpLe0MDDY3tdPUPW+b3zSkxkrCj16I3alH29dDT0UWv2kqjXDofvRJZayvdMoXFaZNk2qnurqWusozSskqqGzssoUCNemlaTyeyfiVqqWxJHvAHm6gtq6G+o9+KtYGJsWE5XTWFFNTJGdJM1kvj5upjwwqGhqVQgAt91sGoVaPqbaKuZrK8N9DUIR+fIjIHwo+VhX/Lvc06hntbaKquoKxViW4eJ63msX7kbbVUFEuOcIspKimnRiprkhnlfBduMmBWd9BYUUmNZNEhCZlS3PCRvyZMS9FdtNLUr6kDGjBqFXRUFFDZ0E2XfIgRZR+9vb1/IcmQDwwxLFkkTOU/cWEmI+bhNhorq6lrkTEgdYIlT/ZDLdRX1tPQ0kKXrIXqkjIq69rpVY4yplWh6mumSmpfFBZTVt9Fz+DohFmqNGiiYnBA8h4/Ns1Uej6Q39kuPYO6IRQKJUq1JAxIUaBGGexstkyNae+WIVcoLeHM9N8VeqVpGkN0S+20shLG65VyyqsbaVeMRz6aPBspQsxIRwUVZTXUdQws3HzWpMWg7qKxvISSifZXWVUjTT0qxibDrRlH0QzJaa1vZ2DMMJeRSRJyVSgH5PR2SVNmW2ltaaFZan81NtBQL7W/JL8e09teNdTW1tPYo0KtNVqc8WnVA3Q1NtOt0CDV2bNv/eS1zv2WIj2oGOpupklqf1VPtr8GUForQ3Mz+LrFPMJgdyO1ZRWUd6i/Mvi6x/xLFjFGN+FPbYwxi1+1n/3Wop0RzWD+w4//Ik3ZlNFcksOXL+VUdErRb37gI707h4cYHh5Bs+C2y7jDREVHA0210pSaKqpqG2ls70c5Zpy37ShNG1W0VvMlr5YOle4bnT8Tes0QstoCimu7LNZo8zWHzCYTBp1ufHDA2mWbdGgls/uFDFhN30epsUSzsrCUnHDqemkuK6a2WY7M8gK1drC526Q61zDQRE15CcWFRRSXVVPT3Mugzvy1nyC1D4yG7wigZsaUMrqbqihp7GdkzntXqheHkLfW0djQTudCnatOP2XTIPLWMgoyP5PdqPqOo8Ppfxz3KTQk66K1qdNSR0/vA1kGrsaGUSr6kEn9tbYWWpoaaZDaQQ314/WFVGfMTi2ddPRZmc5r0mEaaae+pJiaFjnySSu8qVOSopP1I28sId/Sl80jL7+AIqmubBtAGseerzxNZTFtwTw6QF9bDSX5+eRLqaCY0roOuhST75RpOy/WolFJX1s9NZPv/OISSiztuU56FCN8e2zbyKiig9bKL3x+95oXr96RVVhHs1zFt/SKHzn1BQoPP5Kl2FcQ+FEC0ki6Du3oGGOSo5QfevP96LHE/oKAICAICAKCgCAgCAgCFke0OmnwRGcRLwURQeCvEZhox0sCmiRCinb8X8P4v+VfFmFUi2Zs3Lp5MU9LCA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgoAgIAgIAotJQAgPi0lT5CUICAKCgCAgCAgCgoAgIAgIAoKAICAICAIzCAjhYQYOsSIICAKCgCAgCAgCgoAgIAgIAoKAICAICAKLSUAID4tJU+QlCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCMwgI4WEGDrEiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCi0lACA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgoAgIAgIAotJQAgPi0lT5CUICAKCgCAgCAgCgoAgIAgIAoKAICAICAIzCAjhYQYOsSIICAKCgCAgCAgCgoAgIAgIAoKAICAICAKLSUAID4tJU+QlCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCMwgI4WEGDrEiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCi0lACA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgoAgIAgIAotJQAgPi0lT5CUICAKCgCAgCAgCgoAgIAgIAoKAICAICAIzCAjhYQYOsSIICAKCgCAgCAgCgoAgIAgIAoKAICAICAKLSUAID4tJU+QlCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCMwgI4WEGDrEiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCi0lACA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgoAgIAgIAotJQAgPi0lT5CUICAKCgCAgCAgCgoAgIAgIAoKAICAICAIzCAjhYQYOsSIICAKCgCAgCAgCgoAgIAgIAoKAICAICAKLSUAID4tJU+QlCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCMwgI4WEGDrEiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCi0lACA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgoAgIAgIAotJQAgPi0lT5CUICAKCgCAgCAgCgoAgIAgIAoKAICAICAIzCAjhYQYOsSIICAKCgCAgCAgCgoAgIAgIAoKAICAICAKLSUAID4tJU+QlCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCMwgI4WEGDrEiCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCi0lACA+LSVPkJQgIAoKAICAICAKCgCAgCAgCgoAgIAgIAjMICOFhBg6xIggIAoKAICAICAKCgCAgCAgCgsBPEDCbMUvpJ7IQfxUE/q8REMLD/7U7Kq5HEBAEBAFBQBAQBAQBQUAQ+BkCZjOYzZh+Jo9F/K/UiTcajZbO/CJm+y/KyoxJr0M3NoYOhPjwL6Issv33I/AvEx7Muh5kNS+4ceQY8e/aaeg3zqFj1o0wMtRHT0833d0/kXp6GFDr0Bqs6YoGFI2FfE6KITy5hk7lGHPPZOLUjCqGO4vJiAnh4uVE0gqa6Rq1luecS5lngwmNoo3SxCDOX0/mXUUXysWuwU0KBjuzeHjmJLFplVR1j/5YBWcyYh4dpF/eS89P3gPZwBDD2p/hNQ/G/983mxjpqqIs5Qah115T2D2Eas5lGpGXv+XNgwSuPSmi22BG/yPnbZLTUf6GJ5HXuf6mje4h3Y/dxx851tS+RvorM3hzN4bw+I80aE2MzbmuqZ2/u2Bsy+L9o3vcTvpIyZAJ00/k9fVgZnRKOfLaIoo7Nai033iAzCOM9JfzPiaEmKf5FLYM/69pMH29nv/BJaOMlpJM3r98z4caJTrjj4y8mNGPDqPs7/m5eqG7h54eBaoxPVar5/9BHIt/KAMYOylMTuDeg5e8Kuub+24xq9EoSnl59Ty3UosoaR/5sedaP4qhKZ3HcXEkviqkbOAb5X+xLtA8wuhQOa8jQ7j+IIuchqG//hyZ+ump/8DTKxEkZHbQPrhY9ZoJTEraq2toaulm4DuVrVFeSl5KIvHxz3nXaWRs3kbAYkH8N8zHrMesKuRNwhVu3k4no8EaVBMmvZqeog8U1bTRMvt+mscY6qyluuALOXWDjOpN1su7eYT+lnKKsz6RWaVgTDerXJsNmFUV5LxKI/19KTV9BitApY7dKANfHvDg7nOScxvo/e59NaMb7qWnuYq8KhkjOiMGWRF5qY+Iu/2awmETWqOa7rpK6uta6VKbwKjH0JpBauJ9HqYXUr2gRpyJsYE2GtOjuHLzNbmN8vE2g8mAWatkoE+OrLeX3oUk2QCDqlF009+n+gG6Gyopyi2nbcSMfha+mbDM6Ic66f0Qw4WQBJKLW+m0dmtn/gkwoW7JJy8pkuPnn/OlR43SKl8DQw25ZN+/xpnrGZT1axiZfq5z8pWsAPSo1Rp0eoPV8mFSNNKa/ZhL4ff50DxEv1bHQEMOH+5HcuJ6JnUDmp9qq0w/JbNJj6buFcl3Ern3ooCqBd3f6TlYX9Z15PDpwXXOhSWQ0qpHpf8mFOuZ/Ku3GtWo5I3kvv5MvVyNesZL2oxZ00ljaSHFpQ20zsvFjGm4iaq8z+QU1NA4Zz8zRnU/gwVJPHr2iS+NcoaslSPzKMO9lXyIPcfNtBKqu1V//b1j1jLcUULB82iCz0STXNJDl3r8oCaDHu2AHKXWwOxqZy5uE5reGmpqm2jsUmLQDqFvTOPO1Tuk5NbTMqplbLiXutwcGnpUDOvMmJStdOU/41LYbTLq+pEvuFFtQtNRTtmzKI6HPCG7a4ihbz7Xc8/232XLv054GG2k+fNlvH/5lb1xleR3zH1xGLuLKX4Zz/nQEEJC/mIKDSX07AXu53bTau0umZU0Zd0jJsCN7RFF1Pdp5jYOpbtlGmGwOYfMhNP42NnjtMOZfaG3eJjbQt+MGn/2rTUzOtBJR1MDta0Dlsrwa1kxMtxVwZuDS9jiFsbN97X0ff3RUrGP9HfQ3thAbbsC7V9RRY1ddFfd4cjyJXheeM+Huh/rbJnHhtDXPOdu1CXO/dR9OMf1e69432z9RTKb2r/VullLb+lLko478rv3Xd439s8VHkxDVDy7wLkAP9wvZtKjN/2Y8GBopuJNJEE77HGILKO2V2OpdCVxbrSvhZqqKqoqK6n8ZqqisrKapp5hhsdmFLR5cBvoeH+DqEBPtu65Tf6IAfVPvBv1ZbeJObIf32PxpPUYMU7kZVZ10VWVTXpKMikpySQnz5dSSH2bzacqOTrTRCfZpEJe/YGXEcc4eOEBaUXtdA/rrTZWMGtQ9xbx6tgm3PZcITajDtncamceFv8HNxuq+Xz/PKGHQjn7spOR+ToBVi/dgLwqk4zbYYSGhFjSX6mjQ0MvEHo+iY/NcvoXUiStnsv/0o2mMcwj2SQe8+TA0evcyJorPJi1PQzUJHJ88wb2R77nXYPGetmd5xLN2mF0eREEee4i4NJz0jsmW2wm1PI22hukZ/579UIllVX1NLbLGNRL3YnvfExDqHoyuOa8CrejiTzM6+UvP0bGNuqzYzlpswWfmxWUd00I42Ytek0nlZmveJOW8o06Qaoz0kh5mU9Nr/JrvWvSYuz7QkrUBcIvxXMvs5bOEdNUnTP7Ck0DBbyJPk6A5wGOJLfSN2r4PofZmfxfXzdpMQ2kEePvyJ4D14nNH5t7xeYxdMMNfL5+hJCQCCKf5lDSofpaPkyDNH5O4m74WU4/aaB/RI/ZrEbRWU9NaTmVXWPjA0QmGTXv7xAbfIagpEYGR2b2hM0GDeqym1w6fIRT19L42GOtBBoxaGSURO3Cz/8c4c9L6Zl8POae+cQWM2PdJZS8iCPo3FMKetQM1qfy+PxxfPwiSZZpUfbkkBoge7REAAAgAElEQVR7gztJmRQOGDHrR9HkXOL0nkAOR74gZ2ghL0kT6o4y8iKc2OZxhQd5LZa2n3l0AE1tGrcjw7lw9ixnF5LCEkj6UEHLNERGRQW5T6M5e+QUYfc+UtQ53umxftlmtLIamhI82bJmL2EvS6mWGpvf++h7qXsTzcWdm/h1wwEuPkwjNeM9799L6QMfMrPIq++nf7CNypeRhDpvZOm2o0Tcf8bzV+mkp0+k169Jf/ORoiYZ8lETBrUceckz7tx6yvuydnrmDO6ZUNW9491Vf9bZhfFM6rzpDAxWp/Pigg9/bA0mqaoP2byDXEbGlN00fHxMytNHPHo0NyWlZZNf24vapMOoaSM/dh97XT3xCYri/scCCgsLJ1IR5XVtdEgV57SPVCZGh3ppa22ldZ7UWPCMu6F+uNi44ZOQR35VIy3z7Cvl0d7ZQ69Sj2Gy4TTteP+qRZO6g7b8R5zb40dI1HPelHUh00y8IcwmTH2fSIkK52p0Mm/apIdL6li30VhRSWV9J3LpHphNGDvfkBRxlvPXnvLast/0M9ah7i4n96ILuwJjefSlBSvj0KDvpas8iVCbP9l97TPZTX9ReDCNIK/9xOubwRzz9cTxYAIvKuX0jUoioprhrjI+xIYT9ziTnIY+Br/VvzMbGK54zL3YOyQkF9Iy0MNY/gX22flz7mEOhb0ddJakcungeZLLeujSmNF3FlCWeJQ1f+7nZmEXbQsWHvTIS9J4fMyBf267wovGPhRzXtYGdBoVw4MKBgYG5iSFYhDFkAat0TTefxiR0V+TyeN7idxLTCRxMdK9RyTez6CkZxiltWp5+q2fZ3le4cGk6qG7voScz5/5PCdlk52TT3W3GuU8D795tIGmrEt4/Nff2R1bTp4V4cHQ8oEPCSfxdHHBxWpyZse6pSz9ZQlLft+AjbO1/VxxdfXi/MsWaqZ69dLDoGdsRI16qIbcJxc4bm+L3fVyKtr6UapUqNQaNNqJTrJZi6qziE/3wgjycMIu8AoXTvjgu8uPfSF3SauQIQnfc8qABaqRnuIXpCXGce1JseXl8vVeGFF2lvMq4G+sdwrhRkYN8hmZGOjMTyH59k2uPS9DYca6KDLPzbNsNrTTWXaTff/9Hziffce72h8UHkb60eVHERroi7vVe+CC4/ZNbFz5C//PfyxnzTY77Kzeh50cCLvNo4p5OoXzXYNJi3qgk7baMorzc8n+/JmcL4UUVzbS2jvEyFeY8+UwbbsJVUcVVUXZZGVlWU3ZX4ooblWj1hoX3gEw9lOfmcAFt438GfSeoo7hmaMPUnU8WsPb6wfw99iL760qRow/aJ5oaKD0xQUOrN/IpovFVPWMCw+mwVZ6chIJPxtqtfMXHBxMcPCkaBdKSEgYN9/UUSvTLuD69LRlXOfqPhfWe8WR+7PCQ3Esl/f54BoQzbOu/4+89/CqIlvev/+pO446ZoIBFUmSc5YMEgQlRwUloygYAUVFQFRQUFRAQXKWnHPOcMLnXX0ABQSduTP3e+96f6xVq/vs3qfps1PXfqrqqe/Ag3iwnPJnkXjY2WJna4vtT8TBO5LLGY3MidYsZeIJBupekR5og4GGHg4h93j08SvdE9t5LklYnuqh5YEtltrWeCW+pmT0L/TzhpH046kU8cI4o90NVH0u4UNZK/3TC+yw/P349f9GyUoFL2NdsNO3xz61nenlHayP2z7bCj2fnpAaaLnj2mxiooOK4gEOHVZAQVkb4+3WD1NrTKyieFrbS/8vNwXfH0QqXmZ5Zoie5hqqv3yipLiY4pIyyqqbaOmbYmbpL2wcpQvMjnXR9OkDH3dYFz58LKOmfZCRP60kgFQ0y0rHfcIsDXH0vcuj+h+9zUQTX+nK88dAXh2v+6V8+vXO6HsjCOvK4hTLRWG46RtwLuQR2WvKH6zQ+SGdx9dDtwftN60LwQSHxnEzvZCqaSm/NLxJJpjuzyVaVx4D9zukFA9831huero/8UHUQVNhLBeUjmMSV0Vl7zrwMMPCeBlPQ9y55PjzNcHWzgm784lk1fZ8s2hLxUuIegt5GumGo7kVVh5R3HnbSPv4IoubrHZrzyjqo/pZBIGWxqheyKRqdJ51/fpP/IptqkgRzU/INtQNlaV8LhZ0ohI+l9dQ39bH0KxoRxBkm5v9I0XS5VmWBur58rlY9h79QW8rKqGyqYfe6U1KyPf/LVlEMppFvIMuNi4x3Pi8DfAgWWB5qomiJB88zfUxsfMl6MEHagfnWRQ8qiRDVAntbGWEdmQF/dPLSMQ91OXfJ+lKFHGFo0wuipGKu/nyOBQfU2N0o6sZnhac0df/xIgWhmm6Y4utiSMeCXmUjy8gmuqhtaGO2ppqqqsFKae85CXXbU5w1tAVr7hMSqrWr60da+ppbO1haEG61h9Slocq+JIehvlZe6Led/K1PJ3UIHfMrMJ51DNIXU4I/uf9CUl+R9WUBMnyLCNZHjibOOMal0vFNs2y/uTfjxJmuiooCtXgpP4V7ha1ynQ/yWQH468vYXjsKEpnNNEyMsPMbAcxNcFE4yjyh42wv5ZN8cL3u4tGaynNiMTPxgh1HQcC7hVS0j4us7h+r7V+JmVpsIHWJDOUj9oR8ryS+l8BD1IRs535ZF5zwuD4cY5rW+PkZIedrR12judxu+iDj18wMTl1lH/K5HGEA7rHTnBS1xp7O0sszcwxt7bH0f0CFy54csHrMncKGmiaELM83kZH9iWsNI1wjczkVdP45rkomaHz/V1uuBhw1O4hJf3TMoPIylA55WneaMrp4P+ijYbxnZTDZaZ6q8mPsMTpnCmGOhpoqKqgrKaNoamwXzDF3C2WpFc19M8OMVKdQqjpadROa6Jr5YpPWBhhggT54uvty5Wk57xqml9vTNlRMtlFb1kmiXFxxMfFEbedxIbh42iMznFFDul6EnAlkpjt6snK4km894RnlZNM/x+6Y4lneuj+lMo1Jz10tSxwjXhIVkUPo4LhSipG0vuEBFdr7N3iudewIgMZRK05PIqNI+5+PmWCsVcqRtyaQrSTJTZuCTxo3tIvojEGajOJ0jmElut9npX3MTEzzmhnAzWyebw2V8te8OLeJfT/2I9RQDopeV/W5vn3OV3T0EHX0PQ23i5SJKJFFif66Kwt4Gn0RTyszDB1CSehoIu+2eXVvZVogrHWfNK8DDA3c8Xvehav6/oYnd9BOZGKmW9K57q3Ox4ByTytbmX+UzA2arb433/Nm8q3FCR6oaUXRnrVIAOLEuZb3vI+0pq98p7cqx2gb0tzbBpIGz9IZ2h/f4+Yc6rss0qjqGuC+a0Yp3SC7poPvH32iIcPH/4o6Zmkv6imc3Jh1Qg+2kjbq6vYGhtiYmiI4baih7aGCqePH0XhlCbauvo71Fv7vpElhmZhPKoboO/fVIB3BB5WGp6REWbBMXk55OQ2yhGOHD7Mvn1KuKQ1U9m/gkQslsVdCbFX6yKabeZrYQQWe/Zhl1hFSecSYgGFEWLG1v6kC+OM97fR2FBPvWDR3SJ1dTUUxJzDWscMM68bZFT/WEf2nYYmukYXmFtHrqQrIB6mvaqMsncPuBlsh8mps5jFFJCZ92EVSCmro7pjHLEAUEy18ulBID5Wpug4xZPducTE4BdexrriYGyBqe9jSofmmFrezn18measy4Q6WaLtnUm7mA2bkV8BD8vUPw4mwM4MHf8X9Ir5YUO73lY7Hpc76SlPwH7375iHv+ddy9wOAMkOdxAvI5nuoaOliYYt7b/aH3WU5tzhtrsWuw85EJH9jsLa7fqhga+dAwz9JZO5mOXJVipybhDlborOmeMoyslz7KQGujb+hKe8o3JwXqYofx81O/wO2TZ7mdpke5zOHmT37r3s23+AAwc2ykHklQ0wufmVluHFX7TTKni1tCC473/hQ1oADqraWNxtobZ7gpnZOeYXl765aon6srnrbYG5+SWCcweYmJlldnajzDE7O8fiihixTO8TlDQxK0tLLC0tsThXT+mzMM4rq6B6+ROVnRPMLy2z0F1O00MPNJVPc/rkSU6ui9IJTh49xL7dv7N3/xGOHBOuneLkSRXsrhfxsW0VuNiptVbL/z7wIBVQcbEIkUjEQtkNrjnZYOISz5PuJRZXRIglUsRjzTR/eMyNyEgifyHRSemkfehaVWLXH148y1z/F/Ijz2Ghro6hayy3CtuZFC2zMDvD7MwM09PTqzLWy0TtTXwNNbDyTCSpeJCp9Wuy4wwzcwLo+FcACQmixWmG63PJjLRHV/EwvymHkFbVswVIXH/g/5Hj3wIepCxODjHY3rjDulBNfV0eN85rY2jijGNUDtXbrh+NNDR2Mzy7uGFd/EX7SEUsTXbRUZJCjIcZphqnUDp6lKNKamiYunIxsYBPbWN/HkgRd9P4Lp4Lirv4Y88f/LFv/5Z14QAHDmnhGP2cgl/7aiMVr7CyNM/MSC+DOV7Y61lzPvIZBX0zsjk+O7fEilgAecTMdX6mNMoYRUV7wl9WUDMq1Nm4LswyO7fAwtLKd9BZIkK0sszS0iKLU8NMvwvmvJ4+1oEpZHydk5UvLc9Sft8Xfwvl72vC2tpwXPEwh/ft4bffDyB3QokTQrmyCTa+d3k1LOFn0UqynvkngAdBGRXWhcVW6gpi8FA6jnFsOV86Z1iR6QlzLE038v7+dW7F/GpdiCUy/jlF7cM/WICWhyopvOuPm8FZlHS9iCvspmt8lsWFOWZm1tYE2byfpLsolQeXTFFUucTdml7axzden2F6ZlZmjBDtsC/fOGolK9MM1efz8vpF7PVUUT6ugOIxJZS1LLEPvEVaaR8j86L/0/AiyWgLQ5kX0Fc5gcIPetshDuxXQM8rifs1AhjwXVf7prOtzLEy/Iw4e13OOUeTUDK3qs8J3mcbX8BSCSwPUJcZQbCVFmo6jrg9bKJ3dgXRdsCDqImP933xMLLE7mEfw7OinwMPknkWx6t46KSMiXMU8XmtzMwOMl11j8vuDthZWWJpaYmlhSlmRpqcPLhXZphS0THBQijfKFYueF6+R063iHnZBkCKVDROb/Vzkmw1sI0voSj3DvcD3DC1DOBeUylJ5/VxD03lYekACxIxosVxGm5ZYG3qgkf8C8qHRhkd3SBjk0zOLKzpKVIkEjFi0TIT7WW8D1FHSS+M2++b6V8WszLezvhrHwxP6OMa85Ssiq+0tLRsL4111Ke6Y6Nji+MW4EEYi+KZbto/3OGy2SlOnLbE90Ex5YPbGRz+IvAgvNMX+qlM9cfHQofTeo74hocTdsEYnVOnOK1pjq1fONeiYrmTV8KTKA8umWlzxtCdsIQEYvwsMDijjIruOZwvx5OYeJPEm2nkVnTRLcSorkyxOPCWW45q6Oie51JSIQ3T3z1lBSNOcWoArrp66EZX0NQ7zrRgLJxoor4wHvfjhzG9+o6C+uHV8pnVNXVuSYRIFt8pYm6sk5qsBG7fiCbQUQ89tTOc0HEmJDZOtvmPv/eSN2UNtDd/INdfA9Uz2uiZ2+Lk5oabmytuLg7Y6J7ghIIS2q7x3K2c2zj9EfeWUnXPFWVFeRQVFFFU/FHkD+9n357d/L57HwflFVFQVERBQQGFrfUV5GVlp/XtOf+4d2ePzk1P8M99EEJ/5jvecMdTHz11A6yDUnjZPo9UWMO3Ag+CzlqZQJiNHXa+93kpWFJ/ATyIp+ppfBWK0UEl3O7XUN6/wPjXIt5dd8N6w1w1N9ZBR0WRvbsOoKRpiIHZlrlsaYm1eww3c6p/8GySiOaZGWyi9lUi4XYaqJ7SQMvpKokFTQyNjzM2LngHCHN2hOG+r3R+vkeo+SmUT2pj4nObzNoR5lek24QHC8BjN0W3/Qn1DiIgpYSpoiCs1Wzwu/2QO09vEuloheWtWppHhL3KCqPVz8nw1WPPmWDSypppHdm4VowxOj7D3Nr7XtCfxcuLLC0usjjbxpfMq1zSVUPBp4CqzlHmFhZYWFhkcXGJJcEYt9JIbrQTVkf3snv/4U1788MH93Pg4DF+V4vk+dfVd6V0doCRutekJCdxOymJpO3kVjShnlYYKsmxT9ebkMgEbm1Xb70s+R5Jd19R1jfN5J8FVbYM1x2BB/FwA/XvM3582JsRRATYobFXCc/0ZkpbRxgd6Karq2uztHzgY0YARnv2YhHxhuzPHfQOjDI6s8FlaW3TsrKywo+yzMryHK0P3ThvYo99eCal89vVWy0TFpxvL0fJFJKFPOKtddBROMTBfXvZvet39hyQ4/CRVRBFUdcVh4QPLMw0kh/vjL2+AWYXEkipHGVBJEUiXma2s5CXCe6Yqp5F3fUuLxpHGf0B4fnvAg/ShTY6i6Ow+v13zsV84mP7n4LjNwwDKQgvV9FObbvEaG0uL4MM2S3nxZ2qLroXt68rEn23cm/4B9ufSoX46GG+JHngpqvMKTUjzL0uE3PjOlEhbtgZqKGiboFlyHPq56QIXlI//xMqLPA51hxbHTVUTc4T+MMmN4rYm/dJ/TTMyOwvPDMkizBbw/sn97gdfgE3Sy1OyKtg6B1JWGQscfFJpOYUUzEiEB2JGS24QqCFBifVzLALjSM+4TrXr2+UG1y/cZeXVYOrFifpAoszXdS9f8t7wXXxTSpJV5wwOXacE87J3H3ykvy3n/hS20bf9NRmRWd0lJG+FgaKruByVhXLS7e592nwW52JWWGR2qgp7tRyfxd4WOUBmOxro00IF3oehp+VKbrngkks+UrT1zZ6hXjPxWVESwsygGBGUCB+IrMyUOC7ErL65FKk4gUWpzv4fPcinjYuuFy+S35LKVkx4USFfXdZDQkJJCTgPGaqp9HUNcfKI4CQ0FCZrLu1RtxII624bzO4sVMTCfCUZIq2Nwlcc9JF+fA+9h84xL9O//8deBCMG4ICvf1cX1mZZ2WxiWf+Rljb+OCdWsPctuu48H1hXfjz/BLS2Tbq8+Lw0jyK/HFtLNyCCI2MJvryRTwt1TgmfxqTkCxe1o3JSLt27Lr1Cyst1L6OwPHIQY4bXcQrMPxHACz6Dk/f19My/et5szTYSENBGnHXwgiyUuLkSW30bS8SEhNHXEIiCXffUtU7wax4hr7KTG5aHOHgcWPsfcK4Gr9xTVg7T3rCs7d1MuBZsLsIXk7ttZ94W1BAfu5z8q47YKyiio5dIJcfvaHg3XveVnbSOzjMxMQGpUa2GRqiMfcGCc76KGgEkdbUTeuwUGeciek5ZIas9XbZ6fh3gQfBUjY3xEBPB63NheSlBWF/TBGd4BdkF9XR0jXAwPgcYskKS/OzzM3+fE2YmZllZnZBpnhtfQ0InjGLY83UvEwgwFgdbd/nFBTk8ObZLSI2ubGHEOTtiKO5FkeP6XLOyx/f4JANru5hhIbG8bi4nbbtQjY3tZWEyZpH3PMzQV1JmdNG7vhdjSMmOowAZ1OMVIVNjhcJn4bomdnBkrbpfv/MB+n8KDMNeaQ/uEPyupIoHG/dIOnGRWzOamJzMYn7ldPMDHfT271Fb+tsobP2LqGWZzE7F8zl56109/TSP77A4lYSAamYlfkBmt/cIt7dGjXrZAr7Zpla2cbj4S8CD9KFfiZrb+KqoopLTB55rQuIF0aZbcoiKTyYQB8ffHx88PZwws3kJIf2HUXNxBYHL29ZuXDtm/iGce1WFu/6xSzIJtc8s+N9dFSX8SnnMc8/VlGWe50YD1v0DC8RX1JN/rMn5BaW8Km5j4HxaZZmu8jzU0PvxCEOKhzntKoqqhtFxxmXK9lUr0hZki4yPdJHT/tXqj9k88RLmWOal7iW/payjn562+sYfe2LwQlTfFKK+DyyLAPtBeB+q6wszDDz2h83I4dtgQeZ3iZsLOqfEX1Ok3N+d3lQ0i8L2d08ov4K8CAAM3MMv79GkJUOBnahROS1MTE9yfRgCY8CzuHk4IvfkyYmpiaYrH9ApKMxZnYhROR1MDkrjK1SngTb4GDrhVdqHeNzcwjv9cXlNS8gqbCBmmHkcxwh53TQswri8suub6HGCw1PSPU34qTiaTScLxN85RrXBF3u2mWCLjlifuIPThicx9X38mp5ZCRR0XHcKWilfXRRZoKSSkSsLMwyN9PPx0Q3PGxsMY94Q//sLNMC8Ds3w1BjPnkxdqjLn8I86g0FdX2MT00xOdbLcGsuCXaqGNuEEP6shoGtev/KAovjgqduBVX1bTS3bg25aKP8cSjBNkaomgRyp6KDhrYWGusbaGxopn1DyEVLdQU1VVVUC4a7aQG8+/U7aHP//s1PQn+IFpjtLuRxqCPOzv74Pm1hQSpG9LeBBwnTza95fdWKw0o+pNQMyAC46a4KSh+F4/ttrl7i/DkDdJUO8fsxI2zPX+Dit2vf57Nv+AMeF/4Ysj7Z/JbXEcacPHKQA3v3cEBRGWUtY8wEzxtTYwz1dNA6q4qK8kmUjiuiKH+I/Xt2sUvYFx5WRdM+krSGWcYXN3iFSsUgnmKkp5X6D3m8ff2a3PcfqH92AZPTJrhFppCW/5532VnkfGmkoWeC6flJ2j88IM70AP/ac4Sjp5Q5s3GtUFND1SiMpHeNdIulrMxPMFCZx9u8F+Rk3STa1wq9oyc55ZrM7bRnPM/JIScnh1f5Hyhum2Vurp7caGecBE+p0McUvHvHO5m85fmdq1yx02T3BuBB5rWyssj83BxzO8lMO+XZUfjpqSDnmSsLiZreqa6sfJ65uUWW18I5/p3RtyPw8I34cQvhYF/rR0qehmCwT4PgnFoyHt7ipo8VFhYWm8VMHwNNJQ799htHTuuibWiJU2AiNwv7ZZadX08twSK8RGe6O66mDjhcfU7FRm+8n/1a6RLSlU6q3jwnJdAWW0NtlIx9SHyYTlp6OumCZGSQnv6AtFAbLLTVOKtnhYP3NW6kZJCVlSWTZ+l3uRUZgMc5E/TUz2LiFknSmzpaN8V7/ZeBh6lG2vJC0P39IM63qynt/TchqB3bU8JU4xvyQozYLX+J+3V9f8lleqfbShfHWWoU3JTV0TW/iHdSPuVfO+jp7aW7vZHynDiZF4S6tjMBuQP0Tv0CKBBGlXSct1cMsTSxw+FqJpVd3XR3b5aePiGObllGtLfTs8nKRdNIR7KJdzbDXPMsGppaaKhroWdmhYWZAXpaRtj5JfG4ZQGJqJu3EZZYqx5D7oQaWoZbQoKMDWULn9oZAwIyGqkeEoNkmNGObKLM9LHQ00FHRx2Vkwoc3rOHvQoqqGpooaN3Hu/Y53ya/TFsQ7o4xkp9HJ46Z7ELekxG07/T738XeBAx2lzMqwhrrCzMMNU+yQm5wxyUU0LFwAxTMyuCn9RR1bcBbPxpo//s4qoHykx/HbVfKimvqaG9I59kV1vOW29Exs2xtNBHVeEwikdPo6xtstkCZmmFvXckV1+0fw/n2OnfiueYH67l421fvMx1MbK+gJ2zO16GCvx+5n8IeBCLZZ5kP6ypO3o8rLAwOUJ/QzM9C2LZZnSnJtixXPAqE7WQHWDMOVtffB/V/zkQYMcbrl9YYbjiKWl+Jpw5ZYTrnXcUVLXSLszjjgbqijJIclKRzQ3/B0XUTP3wq9dv9O0ona+jMjsIa/nj6IcX8uZLK11b1oXu7n6GJ2bXrKLfvrrNiZS5xlxyo+xRPXMWHR011M7qoGtoipmZCcZ6uqiYxZBR08vQeB2VGSFYKezj8HE1NHQNMdoSjmKkp4mmrgtu13KoEppUMAg2ZJB+1QVDHR10tDXRPiPPoX37OCB/gpMaOugYmqMf8Fzm9bFVHwYRQ58ecO+CCUe1r5IzPPeDl8A2P2pz0d8EHgSX/+WKZGL9nLE000dHQwn5PXs4qKSNlr4JFl6xxDxvQHBe/nXvbX60bT+JF5kb6eTr50Le1/bTW/mcrBt+2G2wpsms4CbaaKscY/8+BZR1TTE237huWGNp7UHsy3rqfkZGIgDmyy28jT+Pk4ExBi6xPClrprmzh+7uDlqrXvP8pg9Wp0+jfekp+c0jf4s3Z9vfu1OheAXx3BhDgwObybp7O+itTibEyhI3/2SSCkp5HXEOJxvLzTqbhRkWJmqcPLKfw3JKnNI2x8rWhUup1TT0b8dNImJhvJPOujIKP3UwPC9iRfx3gQcRs30VfI4346yeDzdeN9DW95X24nRiwuJIya+krPYrX782UvHuKSluJ1FQv0BEej6fmoTyLdLSRkfPEGOLa6EWCw2UZcURYG6MsRB+a2SCsa4KykflOXjoOGcMTdDX0UHfyBjzS/HE5lQwM1HELUslDPUtMHfxIyg4mMAAHxy15Tmpqo+B0zVuPSulQyRmRdRO0YNwQhzMMdbXQvPYH+w5cJzTZ/Uw9ojkSlImPa+90T+ugqGDL/4xN7l169a2cvNGAtc99dE8ZYHDNh4PsmEgWUG8MELXl4+U1XXQPradJ+dfBB7EC4yV3yc57gax11PJeZUt42DJyrhPQqgvAX4hhN54yLPMZzxLiSbE3Rl3z0DCkh+TKfA4ZTzgepg3fr7BXH7wlsphMYtb1RPB0jvTSnGyFxcsTDG4cJvXfSvMLQ1TlebPJd2jHDx8EnULe87Z2WNvL4g1FsaanD6wGzkVQwzMbdbKHXBwciXwUTV1/Rs9fiWw0syrq/acF0CQhy1r4IaIqZYCcuLOY3n2DHIa7oSn5pFfUk5VVQWl73PIirTBSMMQh4Ab3HlZxJfqemobexkTjG7CoiUATH3VfIh3xSswkbT3zXRMroFIK8ss9L3n6WVHnOw8uXCnlJ65BcYaMkm5dpmr1zN53TYn82pbHPjMq5vBhAjGqrx25v5rGUSkSFemGGqupLqylqreWcT/hMfDSh8Nr+K5Ym2MZvAHqgen6KnK49WDm8TceU5x01cahTnb8JbM6144aqih6v+KgrI6mrfO5a9faWkXwMDZLZ7hEua6Kyl/FIqbiytu7he46BtEUFgEEZeDCL9kgvrR45w1d8MjPJFbt+9xL+UR6U8F/qBUkjMS1DAAACAASURBVCIuct7QkLMud8htHWN8PRZRPINk6h0P/FxxMTbA0MAAfUMDTDSPcfiPwxxV1uCsvqDb66FvaoFNZB5FDZVU5ETio3GU46aX8PQNIjg4iItOFpioKrBPzQH3kIe8ru1hRCJhbugrn6JNsDPRRvPsGU4dO8KB3Xv5Q1EVVfWzaGpqoqmpheE5D3yyB+kfq+VVtDOu1uc5f7+O8enpNY/eCTpLnvLwot5m4GGnd8XGcskgDXkJBBmqIe/9joaBme/elxvr/Zlz6Sz9NQW8fnSP5IcFVIxKttWpdgQeRNM9dLc0Ul7bJ3txrlsaJGPVNGYGo3HAjJiPzeS/yeBpXAB+fn6bxfs85200ObZrFyf0nLB18+Ny4hOefhn5hYv7+q/7G8CDoM5Il5mf6qbkjg9+zk6YheVQW/GOdyU1NLR3015TQG6CO0bq2tj4XMYvKIQwf5/Nv0H4TYFXCA2PJeWGN87mZrhde0p27fiGjvkPAQ/SZRaneqh/85ScZ0948mR7eXwvmnhfExR/24+uezRXk9N3rPvkyVOePMmm6Os4wz+nHV7vBBkB5j8PPEhYmuik7YElxme0sA57TPbX6Q1xxFJWxqooeuCHnZoKpy7mUdo9tU1c14bHlC6DuJscf11MLS/i/aCcP2G83HCDLaeiSSSDaYToq2Js5olX7GOePn0qk8c3vHA20MXY4RpJlSPMNqdxxVIHS1tPLsbcJy0tbbPcjSE+0JrTe47ier+aUiHYXTLN7EgVr5NvkHw9gYS4IHxcDFE/cgQ5Qx/8r0QTfz2NjPwq2hZ/tBb/bwAPYia7aviUEkZocCDe586iflwR+dN6mHsFEhAYKrNCNA+vIJFI/qII/SFFNNVNZ00Rua8+Uj24wOzyMisrIpk31OJUGxWvsngpKEDP1iQjnWdPruGuo4S61jnOBSTxZP2a7JjJ8zfFFDWPfyew3NL13z6KZ5jp+8TTiya4eAQTkZpP5pP73LJVYI/K/wbwICMfbXrNq5wCCmu6GdzIUbAD8CCZbqW58CFxQde4XzXJ4Nz66v7tl//65D8FPIj7qXwShq+RJkpWceR2zG4gfxIjmu2nK+cSTtqamHvfIa3m15w2kslySh95Y6x4BpsH3TQO/R0gTMpMdQYZ/qYcPmaJz9007j0R1oUnpFy/Qqj1GQ6cCeJ+WTN1FRmkB1ujrHwO/8R7JKduWRfSUkn0NcdMxwpTn0eULiNTasX95ZTmppGYkEBCTCSxXvqoHzvOaT07nEPjSUhM5kZWNW3Ds9sQ2P4PAA8rC6w0PSf9ViQhvi44WahzdO8+FHRdcPL0IzghnfQP7SxIJIj/4rogpNMT2JbEy1MMVb3m7dvPfPk6xOSSGMnKEktiKaLRFprKCsjeNO+f8TT5Mldc9JA/eAbLsHvcTMv4vm48y+RZZi4lX4cY+tm7UbyAqPMJMQ466JtcxPdhHULChW8zSDTJQFU29x1Po6DiQczrRtp+CNT99fT6d2pIlmeYG6jlU2krvWNz35Vz0TyipiRCzzlyIfwhT6vqKLkbyOWgLTqbrzd+nuboKMlx/KQWOnY+BARfIfblV9pHfuQvWX1GMRLxCkvLax6nfzPUQro0KCO7izRVx/xKLu9aBuipziYzwgVNi2gyyxqoa22nrbWW0rx7RJsqcML8GrdySqhp7aZHyKCzIx8XsNxDS+kLHsVFEhkhuPMfQ/mEHPv3H+LgoROcMnLBM+QqEVGxMrLsnOJKJpvvclH9OBZeiVx/XkFtbRWVZW9JdlJC38aXgDvvae4eZVY2lvupfvWAOxFB+F5wwl7zMPvlNTGyc8c7No37Ga/pkwEPSqjpWWDl4oGHx5q422FpqIuBgRnm9u54eLjj4WCBmeklLj94T/VPeBmkwntRLAAfIlkY2Pz8PBtlsqOSuusmnFY8R+DTEiomN1+fn19kac0jQSoVsTDSTGPDV8rynvI81hXzc254BwYTKOOOCSL4mzdRCAE+Pvj5+hHwrSyUED8P3B3tcb4Uxd2alR244FaYaMji0dWLOLlEcL9O2PjmcNvLAI3DihxVdyToaTaPs7LJzs4mO/M2SVHnMTywD83zcUTdySBLKM/O5nnOSwpqBI+BDVlzpCIkgy9JumCJvXMEccXr2XmWGfp8lzv+xqgoq3BMyRAbFzc8vLzw8rqAm6MVpqcPcPCIMpqmtjh5eOJ5MYhLl5/xeWiGVYqUFeaHm6hO8cLJ2AS7i9HcelVJY/8oM13F5N68gJOZOVZuV7mRU0RV+Tsyrjrg4nAJ/5tvKO0eZ6b7E6+TvHGzssTOO5GUsqH/etpNqWhF5nmzCq5sw/Hwl0ItpCx05fM8zhsbCx9iPo8xPPaVD8l+BHh4cyG5mJbONlrb2mitzubhVRfM1AywuvWZ4upm2nqGGZneDkj7vjpKRUK60nmmh7vpriuWkZ6+Kyzk/YePFBUXUZibzkM/LVTPmOEWlc7zL018be2gs7uXvoFhhoe7aS3P5fmtq1y8nMHHlkGGBc96geBTsoBgtHj38DbJseEEeZzD4ozg9aTAvt37kT+lh5GTD0FXY4iJjeNGdiX1dQW8veOFtRCWfaOUgk/V1NaWk/cggkALZeQcH5DzsYm+iTkWpVKWpodoL7hN6p0bXPW0wFztJEdOmuIac4Po6ze4kRCEt6MhuqoG2D4epXu0Tubx4GbjwYXHHbI90yp4L2a05gWZfgb/NvAQqHuKg3YpvG0akYWxfm/lv3Am6qAkJRgfYy10LH2ILV1gZBv9ckfgYbHnPQWPbhB05RFvO6aZWhZioqUsdxdTluyOgsIF7tX00SbEzPR20N7evlkaC3n/xA9DIdQiPI/Mkla6+oSB9GeVvr8DPAgNJaSCqeNlpBte5/3wSftC87NQIhJf8K6+h57mj7xL9Mbm4i2yKpop/fiG149u/xADk5ySxbP8SkaGK3h5M5TYOy/IqR7dsEleAx4czdHyTKdhZp5JwV1ctvDPMCi4gzv9gbKBP3EvKukWSC2/vRQmKU/1x9d2G44H6SyTfaVk+lvharPFm+Sbd4k5pgaaaJ06xK5/7eLACQGBM8H82/Wt3xM8U5yIyuug6WdWnU3j7D/h8bDEdF8lL93lOaPiSPDjUpq2MhtLp+goekCs+Ul2nwnlcW0/w1tR843PKV2ElXrSPbQxtg4k6En9Ni6HG7/wi/NvwIMGjgGPeFz/fdyK2x9z84IN1vahxBdW8eWGJaY6tngnv6N0+EfXWulMC12FERjvO45nSg1lGwjmZPGgQrzt8lcqnq+RS4Z/obZ3hhVBkfkWP7T5ef9R4MFBm2O6vtwuLObdp898/rydVFHX0sfI0kYQRIpoaU4W697X287nJBecdTU4axPGrS9ddHb3MdTVTH3FZz58cwlbdw37yfFDCR/qhpicX2ZxoJSPaZdxsXDB//47ilpGmVj4CT+DdAGWS7l/Xgcrx3AiXnVvmKub2/CXn6SLLEy28zktiZziZr6OTNNTnk2ytTx7/ic8HqSsTPfTnWLLOTMPAgQPgI15XncCHkbKKHlwEUMlVYzjKqjqn9tmA/uL1vkPAQ/SmVJexDhhcdYUg6slDAhx8hsfRTyHZPAZUTYa6FkEEZLZJgMkf2Y5lwyVUHTHHe1jmrhmDdPyt1JSrgEPgbYcPRtK5vA8q7eTMN1WTOEVXeTUgrmT/5pn94LxtjRH1yeb6smlzQRqst8kZjD3MiGOjpj7pvNlDXgQtrGr3CkCa/84Cx9DcRXIJYMfktWxvMqVJITWbvuj/1ngQdsygODkFxRvuyZ85nPpF0rrehmZWfreT4LrrkDEOtRPV80LsuMcUDt4FM3gPHLLWugZ6Kazo5Haj+94/1fWhfeVNHSNMCW4Uc/10fjoEt6ul/CNyeCN4Ca9tF2M7vfBI+79QNEtV84cNSG0cJj2nUgWv3/lhzPJ0hRTBQG462lgeOEm92u2egJIWRyooeG2Jcpyqjgnf+Rj/6YR/MM9/6kCwVg0+OkmAd5xpOXX0zaxtOpdujzDzLtgLli4cTHhFZ8nZpka6KSrc4ve1tZEe2UyIRYamFoHEpbVTEdnF71j8ywI/ATTQwy2VFBcVETRmhR/qaeuc0zG3yIbjn8HeBBIvrs/UJDkhd4JfS49eM+bT0W8uBmAt6UxWj6ZFOSmkHb3OjFRoQR4WqF75A8U9d3w8L9CVPQd7j/7RPO3ebRdy0oQixaZn+ihpzKLRFcz3ByN0NbU5JSSHpaB10nKLORjTQfdI9NMDX2lN+cChkcVMAnJJqte6O9lluc6eO6lgs2lRJIKe76PfZZlKeKH+7tpLs0l2/sMx7V8icksoqZ/lOHuRkZfe2NwQh1TtxDCklJITU0lNfU+qXf9cdI5hcZZS84F310rTyX1YS6FlR0MiMRIRDMMNJZT+XlDH5R8pqZrmsmFFaYHWmkseiFzzxZctNclMyWRZHdVFA5pYR0UR2Lm92uyOi8+UFrfw/B3NQfBe2qwNJ0UdzX+ULLC8/I1IuPjif8zEnEBF1MtNLWdCP64xNhW/W69a5b6aBVAwoeZvGtoofSuGy46R9m/RxU1y1heLfA9Naeoi/bSRDzkD2N7s4bS7u34LNZvLFAPLDJVfBV/a0vsfO+R3bke1rLIaH0+BY/C8btwDo09B1ExtMXa0RVXO1PMNZXYs1sJ7XMO2Lu64WJthLG6GvuUg3naNsq6iieEiyz0lZAd7YaTiRl2/kncL6yi+5U/DmePcvyMMebn/bkWHUrwJQdMdUxwvfaMF7XDzEx205N/GQ9TQyzPXyPxZS3d/0cApayFpIuMdzfR8KX421wuKiqm4ms//eupCrYjl/zTwIMU6eIwTdkRXHY5h45jHM+r6yl/n8RlgYTSOZDLGYUU3o/jZlw0UYFO2BuoclRRHROfKMIiIolJecPb2sGddROpeNXT4cNrXmwY6+tjPicnk/S70YQYHkLhlBXnwxJJyd4y7oXvZabz9OFt4m49JuNZDvllLbQOCUCrsKIJPGxzTPZW8vFhJCHWOli7mHHyyCk0zdzxTnjEs3eV1ApZu2YXmazPID3IFJUzJjg8G6N3SoRUOkNr4V3iHLQ4HVbG1+Ht+NcW6XoVxTVHC846JFMyD7L9+mIdJY+CcdW2wPPNPP0TDf8x4CFA4wi7lZ0ITHrO67JmOoammFv3/vg+rX5+JmqjODUMP3N9jO38iC6ZZ/jPAw8CUvWWl7HnMT1rhMHVj1T0zTAvFjFZ95KXoRbs147jVfsYE2KJjIBoZQsj06+yWkhWFllcEGJFdoo9mWV2ZoLGB+dxNrbFNuwJxZM71V0rX1hi6VssooSVzsfcuOCIi+d17hTVUhp2Gg3rSO68a2F8YYaF0W6+do8xMjbBxMS6TDIxOcuiSIxILEIsuM8tLTA/0ETz1056R2dZ2KRHrAEPdvqo2MaQU1bOpy/llJcLUsb7V+kkmO/lhIYLfokZFHy7Jlz/zJOrzjiZmf5ILildYG60meIHUSTG7kC+de0qwZ522Kjt41//+he7FA2wcPHl8rUd6kdGERl5nWflg/RMSxDi4ERLP+sDoV1nZFk7sv0N2C13gaSyVtqmf9IP80JM/yqp4I4jVDLJaFsu8Tp7UDIM41ZB8zbuwGLGanPJuqjO73ssuPahjdadXl7CP5LOw/JnbjucxeRcKGEZDcwszK/GNs3Ps7C4zPJ6poQdH2zDhT8DPNhcIOxJGoFqh9BxSeLhp+7v6d423Eoy/ZXOt1cw3HcCz9TaTcDDt2rbZrUQPHc2cJd8qyx4+v2DoRbnjrLnjyMcP63MaWVllLcTFQucwx7yZni7lHWCyW+Id9fMsTp9AjXbcJJrZ1kQS1muvsd1TwOOHz7EoUPfReBd2bv7d377fS/7N5QLdY6cUOf4pTwquydZnmqjPjeBQBNl5OQ0sI/NpaB5jPmdOCz+SeBhQ3uvni7R+yWTW5Zy/yPAwwrzo83keyuheVZwNf1My0bFcQfgAfEgrR9vEyYoeAZxZFb3M7EJL1td8xbmfzLP5yaZm67liY8hltYXuXivnIkd1/LV+wgEiiub/s8PDYy4O4sH/qboCoR1T/qZ2ZqJQ7oEKzWylFOGes44Xy+Spen8ZnH+8ZaIe97x7roTKscNuPSqn8bBORn4OycAwAJBrGhngO/H2/054CHxdhBBXpYYmvsS8X6M2a2/Q3ZjEf0vQwl2cMTc7zHlK0Kuis1/22e1ENa7jQDgxu/8k8DDEU7LySN37OT2a4KyMmfUdVA7n8KbxqFt1j4Js/U5PA/W4+D+o6gE5vOhZYKl5U6+FiVy6eRhjm5cFw4e4OC+Pez612/s3ruf/Qe/rxeyteOoHT53C6lZkSBemmSw5BYRjlpoqBpgcvE2L1unZZaanUKk/z7wIGSyGKXlthWW6gbYhGeQ/y2T1vc+kEy1M/wuEGOFwxgGZpNVN/vdI+J7tX/4TIpouou+95Gc1ziFgVsit993ML4sZIgYo/2+Lbbn/AhK/UynSDDqiGTcK5KNjfXTrBYSpupe8iLMmBOKihxVVET+8EEOqzliH1MoI9mVTe2/AzxIhmnKjeeKqTL7T1nh4XcJD2dj9FTVOGviTeTHPioeeHLJWgMlgbvrj93s+kOOoydPyVyUDx9Q5+y5eF7NsQ3It9bcAjfFwjiDDQW8umKJroEPcQnCXLVH44Q2dp6WnNUyxykklcyyNjpaS3kffIYTh/ZyzOEWt9/3sSxdYGmqhjRHZez97/Hg0/AGD9j1bt05q8VYno+M48E3tZjSMdEqgadoEfHMa5LdjLBziCDmw8w3onYZ+afMm2KB5dlGsnwNsFA7iqKiAvJyRzhy5DhmN6r50j1Ba+E94qzkN71rZXPn4AEO7N3Fb//6nT1/7OfAlnfuITlTzsfkUDK7/vzCcQ14uKDJfrWLxKZn8fzNG978GcmM5rKrKfo6vwAe1v6dZGmMmbYMwk3PYHBaHjkFLdTMrvJsZIah6TUOmMlG6gpjOS93COvYTxQ2ja2RS65xwAiZxL6BsWLES2NUJ1pxTs8am5AUXst4GLro7OyWcczMjDXQmBeB2SFNgl520jguQdxTTPltT47Ke5LSMky/WMpc02sKrtpxcAvwIHt0gaduupEPd8KJjrxFXFYxX9POc87UAB3BDV9fE60ziuz/QwEV59s8LmlnaGGJubF22jJ98Qq4y5OSjs2eihu74D9yLljz+ym568dFvWOycaSoKMeh/QfR8XnIo4o1L8K/AzwIoTR9edz3MUVfXRNNW29CfVyw01XipBBuEPeYgppckqxV0FdR5Mj+vezde4ADCkqcPn0KxYO72afnT/CTWjYkc9nQGquG6b5sf3wtVVD4lgDhCHJHBF6/PezZ9Ru/7drNXiEr4hFhnqzJ4YMc/GMXu37bJXvPHDh0hCPfvq+Ipudd7pcIc1pop2UWp/uoz4kh0sMJU5tAXr0IxFBZD2MTQ0xtLNAz88TvXgm1Q2M0voziqtVR9h3VRDW8gubhBcSSERrybhBupYt2QuM3HpINPwbEQ5Tc9sLD0grjK29l66mwzZSMF1OQ5MM5LWfCK5cZnv5PAQ/x+Kvs5re9Bzlw5CRnrf24nFpIeZfAW7HMynp6+00Pvd0HMTO9tdQUveVVYQP9grr2bU5+r7+jx4NUPM9I3UuyL5txTE4btwflVPQN0/r+HtcdtVH0fE3twAzLS8MM9XfT2LYx/EBIL7hTOk1hMyVitPY1rzPuk5ycvLMk3eSaixaap9RRM3Un6OZP6iYncyfzIyVNw8io6aQr9GRewsv+Au6xryjrrN8EPEyIxcyPtFF51xNfN/vvKf7sXLF1us6Ltl6+9nTQ09lNe0crjbfMsLx4j6efurbEbK4CDyHGh/h9zwGOyMsjv0GESXBgz7/Y9fte9h08jNyGa0K9w/v38scJox+BBwFvk4h+Tr41PUj962SuGe7ltz172KNgT/DDj1SP7UTWNYtA1CVYLwQX1/nhNr6+e8jtn/VBchIJYV54GZ1k1z5dHEIiiUr6ST/cf8Td1y0M/cxFSjzIYFMa/kd3ccLqBg+LV4mFvg9L4UzCXEsh78MN2PO7OpcyG6n5maVSOgOL+cRZqqFv6ILzlftkPn3Igzu3uffgMZmvy6hsG+FPc339GeDhnDuhj5/ib2hPYn4jjRPbp/iTTDfTWXAZ/T9O4pVWy5ftmPO3Ax4ky7I4wIVtXCz/UeDBToUjJ825EH6VKz8Qcq6BWNHJpL74TP30NpldxLNIxnOJt1XlzB+72Kuoh773E75MrDA9WE/1hxc82ZT6J5UbgQ4yNngFDReu3EvhXtr31ECPnmTzpKiT4eklJIKCPNtPX1UmN21PoX5GD7urmeS27KDM/78EPEgmmOjKJUb7AKpW0SQVtG1em3YCHhAx3fqBwjBdDh3Uw+9ZLTXfEkYLhEpDNBZmk/XgJ/M8+SbJSRG4G5xERVUfPacwbv50HbnD07c11G439jdM/JXae9xw10Pd8AJBb+dlTNMbLsuUYcR9vL5ijpWOBeeuvEDI3vUzPEPUmsuba9YoymvilPCEO2kPSXtwj3sP0knPeU+JzItm+7m7+X8Ln/4M8BBEQlIgl6OjCbz+jraF7cA64V4i+nJCCBKAB/8nVP5Z4EGIA14QcnWLN1ha15/0nwQe5FHXtMLKPfgbidsPWWmi44lJLaG+f5tQOOkMLbkJRBgf5F+/7WGXvCk+D8uoHBhlrK+GjxmPeLxhXUi7E8etIHNOHzyGgetlwm6mbU4X9jif4nohNlboBiHcYo6RumweBNtgoKKFhtMt8rpmmdhO0xFsV3/b40HC8uwgnyO0MFQx43xCLl+24XKWLvQyXhOP0/HDaLreI/XT0E/H53rP/e2jZIXl6V4aHrnjbKCH6cUkHlQMMj/dRq63KuauMVzPa2VeNM/yVDs1jYOMT2+wGv8UeJCyONRMc+HjNcLxm4Q56aJv5ID1tbcMSdbm4N8BHsQDVGVGEaBzjGNqRlg6O2F6VglldWusr+TStiBmYW6KqbEmPqSEcMlQi+Mu6RR/7aGrIJ5oZ3u0fwE8SOa6aHidyDUnI5Q1HAjJqqOmOIV7gW6YmIdwr6mNkoxwAlwuEZr4gKcFaUSc3csJuQPs0wog7HEVI6IpFsYLiDU+hcuVLDLrNqdaXO3HHYCHiXbGXnqhe9yICzdf8ebrAIODgwwO9DDY+ZQYBz2sBG+TF52r5cK1wUGGx6aYmltEvDRMQ346GQ+SSbp1jfAAOwzk5TGLr6S0a5bJ3gaq32xNtZdGys1I4lzOoHDoLObe4USlfH/fytLyPRK8Ktrp34R8rgEPrqfYc1gNfctznJNxLaxzLvzkaKWP9pmTnNH6M8CDhLn+Or5E63NW250gb3vcbfRQUDLA4XIEoeERREREEBHui7ebCWp793LK1BM3vyur5VdjiYzNoKh3hol1o6B4muWxdyRanUblwF727DvEEVnmCQGwOYlt9BteVdT8feBB6GzJCgsDLXS1t9M4MI9ofpKJ8XFG+2spenQZH2MNjppEkf7qGakpz8h+W03b3DwrE41U1/cxKKSK3WZz9rfXgx1vIGyop+mqyCfvYTJJSfHcvH4Jy1PyGHunkPrlnwAexIi7H3PD0xytU2fRNrXG1dkAZTk5tDxuc7dkkPmVRebGhxmpfUCkkwHGlr4E57QyNNRFToAmxvaBPwEeVt8B83111JW+521+PvnrkvuIW/42WJw5htwpYxyvPyfjZR6v3+TLQLOXT+9y202Zk8dU0HcNJzrt5ffv5r+lqLqDToGkVAhRG6siN9YFeyNTLC8mkvK+moniYKzVzuF9K5us4tfkxHtjrOtN6udC7kacx/H0Pg4c02C/RQofuiaYW2mjPCsCH0MjbJ+M0vtDKggpzH8mI9QGWzMPPNM7voXcrLQ+JzPcAz3DEB4OiZlYELJaOPOfCLUI0lPisOMtboS54W6syqljZ1AxuUjQwy80DM//CYL/1QG3mvVriUUhc4ew3d9mHO4IPMjiq+cG6Sl7yi0HVXRMPAm7d48bcf646xtjndpN9+Qio1UZPIkNxC3wPvl9K8yuvfR/BjxIpUt0PL9CuJsZ+np66O0kurqcPXmEQ0L8ncIpVHV/UldPDwPPRJLftLAkXUYq6iU/1AgLPWOMzocRl3CNy2ZyHFW1xjkghns573iR/5GPISroW5zHzS+C+KggQi7YoXz0Aveqy3j2KJEUwRXufT21MWfRckwk9UP7ZuUeMWNfP1GcfZekH0iCEom7GsQFzd85omSElWcoUTd/JBJKSsnm2cdWmcXoZwr01v6TLrVSkRnOBXVFzrh6Y6ysj1d0Nrntq8y+W+tv/ixmsqWId7F2MnKUHftATw9tdWVOK+znX7vkOKEmkKHt3A/65g4YhRfSNPgTghJxH/31d/A4tItTDnd4Wtr3g6VPGH9LHcWUxZmyd9cJXNPqqBBIGXf6k8zCfCG37DXQOnESpbNGWFhZYWVuukoipW+Eqd0l/BKy+NizyMx66tWd7vcNeFDHxj2B629avoUStX68wRUHcyzsQ0n43EN9WQ0tVW/IeviQ1GcfqJnYwIwrvJemmuh4HYruHye5+LCW8u1+xzrwoKyMWkgez7OfkZkUReLtJ2Q2rE7gjY/6jwIPjvqcNIkgq6mdxs4t2WnWs9V09zEwMsWsYC3b+CACD8PsIMN53rgZKSO/Xx45+dNoW9hjH/OW0vZ+hibGGBkaZliQYUEGqMmMJMLRmFMWseR19tM5NCgjROvrG2B4ZJSRqUWZJVr2r6QriOYG6avMICkkhBvp7/g0sCGWc+Pz/D8EPEim2+h9dwUzuYMYBmWTUze5eYOzI/AAoqlWOt9exlJeDoOAp2TVjK7OQYErRdTM6zhvLlro77w26+mip6vBKfl9HDgoxxEldXR3Wsdl5Ya4RWfxvPHnEdiThwAAIABJREFUDMHLX24S66yDqok3ESVLsgxDG7tXtr2RjvM+ygpbbWPMAzOoFB55c6VNn1ba82VpmY/vP8wJLROMBFJBC1NMDPXR1dHDwNwRr6gMXlX1smNq+G93XAMeAqxROHORpIoGKtuEMMM2at9n8Nhbi8OqQdzOf09p61fKPxfxOfsGkXc+Ut0zuSUvt4ie58EEODhiEfB0lVzy2/9ZPdno8WB5MYbEV2/JTb1OmF8yuY19/Ijj/LPAg659FLGZX2hfXwe2Hrt76B6YZHbpR+BGOlnO66RLWCsd5si+vSioaqNpGUjk42IZydjUiLAWDDI8PCRbFwbbymjI9EZXXo3zCXm8qh9kaHCQgZ4e+oaGGByZYEpIVbphARKyHfRV5pCVFIlXaBYVIwvM7vCa+CeAh6WZfgoDz6Cnao3HrQJqNm3U1jpveYCp1jtcUDqMun0idz/0bvN+29LR/9BHqWSZhcEK8mLdcLWyxsonivTCdPy1lLALzeBZ9QRzw800PvbFyTmM2wVNtE2vNdhPgQfB+DfP3MQw/f199Pd3UXjDBVc7J6yu/kPAg3SRqYF2mss/ycL9Sp+F4m6ki6lLFInFQ99Cqpa783l21QULI2c8n/cyNCdiqeIOty44ofMr4GG0lLcpkfi6hxDzvErm/TTd8pKsmBDcvG7xYniJ0aEOmmvqqSt5Tt49f/SPaXEp3BVDE0/84nMpnxhitiMVH/WzeF0voKBru0GwDjyoc1IvkKj7D3n+oYQ3eW8ZzHRGQ1GRE6o66JpZYSXoKVaWWFlooqxwEDkZsafFWrlw7RwuoXe486EfkWSZ2fEhhgf76e8p5/PLSFyPyWGRIAAPCzK38NmJ9ffs+nGI3vqPlEYbckrBEp+UAj72rl9bP47LUoJuVovWgAePs+xXcefa/XSePN/GVX0bN/fnDyMIdjJC71ehFrJxL2FxsIWvdz0Juv+RvLSrxLjqcFjuDHr2DtjaO+Dg4ICDvSXmRmoc37MbBTVTjCztVssdPHD2uE5O2zjDsteLlJXJTvpyfTh3WrB+n0bDwoOghCjio90wPiqPdVgOmaUC8HAZkz8UMXYPJyTmJokR3vjZaLN/nxb2YVFcS7xJQugFvMx12L+Tx8NUE8WpVwkPusaVh1WMiiWszHRQmR3L1Yvu/x95/8FVRbL+78Nv6vmeMzOOMyZMqCiSc845Z1AQEQMIqKCCChgIooIiIAgSJUfJOeecd7j+qzdBojpzZs6Z33r2Wr12566urq6uuuq+Pzd23lEkfO6ityOTuCAPfC5H8TC7jv7s6/i5BxD6opCyntlvfsP+ourh62mkIpZmJxgfHmCgv5me1iSCtOSx8H3K078CPAgDp4v9dDXWUPH5M4XZKbzwUuT8RTsCnhdTOyGIxQtW17O0vvTC3dQSu2uJ5A4LoX7nyL+hgYndd8CD0LZeWWBhbobp6UkmhrvprnjNk6t2WGpromvpS+DTPOp6eqhKDSfiegQRz/Oo7O1loD6Nx76mmOsZY+axps/RPjLN+NQ0M/NLMst56cokK90phLm7E3j7Oa9KuxgcGWCp8g7eZt7cTvlMxcg4Yz3NlBXX0FIUzQ1nayyNjXEN9EDhtBfPagboG6/gU1wA9po2BBQtMrzT9UAqZaUpnghHE0wdQnlYPb/exhYzWhTNAx871O0fU7ooZWHlbwQPOhc46pVBcUU1dfkpPAtxx05biTMXdbHwiuBRRjWNwwvb25hfS9QfmvsGeBDOI2JZEHf7eJcAEy1MzXTR0DNGV9+PBw3LTCyJmW3LJi3cDXNtc2wiC6kWzFgF/9PvWDyM12eT8+YZsU+e8GTfKYZQpzWLByUjNwIffWvfJ8SlFVHSPIqIVaSiIT5FOOBhZYyRpQPOdpaYKR7k9xPKqBvZ4BHymIin7/nodwIV21AiUwpoKHtHZrQ3Cr/aEVP5iUdhPly/fJPQ9/XfAA9CSJRppkYH6O/v3zH10FyRzXPrXzij4cXNxE/U9e7cp5/+oVFGpxZkFc+WNtV3HqSU5Z5s3oU7onvRAPe3xdxzNsDJPZTb6a1Mfsv2WHZmKYujnbTmJxMXG/uN5/CYqOveeOqvWTzYBYcT/nj/5xD7/CXxOW0MC6PV+92BSHjxo3H69V8o2Mfzqnxwz0p3pbuEykgTfvm/49jH1VG+RRth16mFDtNqD9Xpz3kR85AHj2KJf/aChBfPiX8cSVigC/ZGWiipmmMXlk1p78y+DVTZuTfBgwJqF/UxsPfbFB71czZAS+Ei2rYhPPqyyqpIzHJfLskhnrg4XCb4VR39S9LNBrJkqomOjKuoHziHd8Ju8CBemmGir4D30R6YyB3iqJYDNrb2ONm74B+RyNtW0RYTwrU7/0vBg4MB5y2iKZ4X7YBqu3J59wrJPLP9ZaR56eJmp8N5BQ2U1QXrCW8MdOy59qyQcsGdqb+ZmqyXJBR0MjQzT8/H+9xzNUHB6hGlc4t01GSS+T6HrPKerz6dW68mhDYSjdNZU01zez/Di0IIqCWWB+qpKS+mID+ffNmUQ35uDJf1FVDXdcTxdgp5m9vyyS+soKa5nzEhCMrW8//Q/D/J1ULMXFcxheGGnPhdHa+ESipHdoQh/QZ4QDTFdFc20abHUdYPIDS1lrXgI2sRV5oK3vH2eew36uZHPHl8CzfdM1y4qI2243Vi9q3Hhfoijtef6qjf3VPelvPLpZGE2WlwwdCP22Uru9XQZbXKDIURFlir6WJ4KVEmyvgt8CCe6qKjNI3YyHs8iIklTiby+Iynj+8TedMXNyNFlNVNsb+ZzLv6se9E51gHD5d0OHhIGSMPQblaEOnzw8vJCku10xxQCCC2rIMB0QqzfZVUxLmgp+PC9YTPVA3Mrdd1QukT0Z0awGUbATykUCNEtdjMDWFEf46Z/i80J7hhoXQOBWVdjBxdcXRwxtb9Me8bB2QmmZuHyGb+WvCg4/qYp0V718/br7t1Sbi3VUZKonl43QEtDW105Q6j6nYDR3Mr3LxuEp5WS8/MIvNtuXxIzyO/rofhiXaGPwaiJ6eKZ+xnCps76a7IISEunfLRRWa+Zs7WiyFZGGG0u5HP5V2ML4kQSQVLq05aKwvX64S1uiHvVSRRPoacPKyM3b00kjI36ox8CgqLKazvZ2z2G98txCzP9JNz6SyaFy3xiMlli/TP1zSJhpjpisNL/jCKFneJye3+r4EHWa0mFXzY35F43QlLbRX07M25cEKXy88+Uz6yyspUF50fQnDVVMXS7xHPirsZF/rO3wEPX29QeL4rNDz3wsfeYQd4GKRKsBgw0UPjdhUDMytI/mg4Tckyq3OdlD2ww9HGh4DHn6iV+YKtXbcnK4TrlmooqNtzOekT2Z8KyY+9jK+JHgqanoR/LOJjYRFFRUUUFZdRUd9O75wUwRtYOt9Fa3UJOR+raJ0ShIqHac1+RGxEJGHPS2lfXPtuC2LIq0OV1L67h719BNkVrwh1d8QnIJpnpV8YKwjG5LwZVxPLqJoS0rX1J0G0NMVATQbJbvKckNdE18QCp2sPuRP7lt4Eay6cVELTwgm3K4EEBgpTAIH+VugpHJeF59W09V9fL2wLIuRxGmlVY9vbVOJeOkof4n36KGYy8LDfgNMfiWqx9T7WwYOLIr+cEjpTN7gZFkbYj0zBLtjpq6LyPfAgXWJqoI2GgmzyMjIo6pikO+8hD92MkFey5+qrNJJT00hLSyPtzWNiwpxl4pJqzvcIe/KKVGF9WgbvMkppmliHjqJpxpo/kOR8jrOHfubfh/SxvvaCjx0NdHyJ45KyPA7BG+DhGvq/HELF0B5rF088HUyx0DzHLz+fQ9vWCUdPT9xsjDBWU9oFHqSrCywO11L44ho+liZYuIQQkVZFd2sRWfHX8bPUQ1/fEpuASJ6+fkPykyu46F7goqoNNjffUFf2jFsuxlja+RDw6ANFXTOyTt3O0rT1ifwt85JpRLPZROifw9pvO3gQrBbuOplj7XKXuMZVhBCuq1X3uGZpjbVfHOkjwkslRtz2lDBBu8HlHvGCCaLst+YmLFkYoK/sBTf0LmAWkMzbuqE1gU7JApL5apLc1NBWMcb80kNefSqksCCHB44KqOnZYBv8lFzhPV6fissbaemb2AT4gs7GeG8jtYXvSX0aSdhlJ+wsrHG+HE5USi5F1ZVUZz4h3NUEO48w7r6ro3d2lvGuWqrzkokN8cHTwQ5b90BuPUzkZVYJFS0DjMyuIBYtIJ6o4VNGHmUNfQzPzTI3VE3xQx8CbyaRVtHLiExUVyoLO77ckszj23e4HfmY9A+PcJZXIeBVPRXlaaSGeWKg5098j4TJbWMva4EQet/44mlmhc3VZAo2LLslY9QkBHDFwR7jW3kMCUFaRE1/n8XDlqgWK4tjDLUU8zHxDjdcDdBT00Dfygv/qDdkVPV/W8D3Bwrpd8CDYM6ygnSlj/LHrjioHuPgIRVU7B9TsuFHJxqju+QF0a56nFJ04c6HFtomVxDvCx5+IFUbhfbPhtMUuhTSZXpK3vDuRTQPHz7gbkggl/QOI6dghKVXMJHJGbxM/0iu33FUrK8T8SKbysJXpN3z4JwAHip+BDxImOv/QlNDE80943t0mMRM99WS7nSA83qB3P/QuEdDUczC5ADdXyop/jLE9NLq9o/LftklnafvYyR3nIy5aHyDlP5FqlMCuGTvhENwMoWD+32E9jvhfuv/BnFJcR8D9TG4HPwX5+ziSCkb2NLg/pqOla5iKu4a88u/TuAQ/x3wsHGYeBWRYIK8tfaWLDHdU86n2ABc1U/z8wlbwnM76BDkr/f7bQEPSvJKKOlZbrrjWBoqcf7kOTRk4GEtNrV0sYvKlJtcEYRCLW7yrHKEofm1UUDJZCMd76+gekAB78R6KoXKWrTA0kQ3TVWfKcx6RWJMEL42GigI2gcn1dGz9SMoKpl3n1tkjaattyMk+Z8BHgRxw3baP93HQdWcsJtOmBtbYGAbSHT+e6LtlTH3eUpScS01Bc+4Y6WO+q0Savun6N4EDzGUzs1Tk+yDj6MHTmFZtAoicfs8F8nCBBNj44xMzrM4P8FUcQyR1y/h6e6Ou2xywd3NHM3Thzl++iIXDexw29zmjrtnCHcT8qlf3qJEv8+1dq/+B4GH1XG6ip9xx0Se3xUDiSvrYXDnwNu3wIPQiZruof6hIQZKBjiGvyN/J7jYnQFf1/xN4pIrZVGE22uiaOhHaOnKDj0d4fJCQ2eagnBzGXgwupQkE2XcaOp8TeD2OalEgmh1rW7deJcE8bGlsRZq0m7ipXuWCxrOuMd8pl9gL9sP37K0AR60OHBQHhUTc0wsrbCyssTMQAvNc8f5+ZwAHroYkkgRzfYxUBJDgMEFdO1uEfG2jq5ZARAJqVil6/VlLtvYYnrlFXUy8CBlaaKH7sYyinLSeP0kjFBndS7IHebIyYuoW3hyKSyepNwmOicWZKJ+WxIngxn/63CagiulZK6Nj3fcuRLojtUlX1zkT6IfUUhy9HVuerjjGPiCgv5R2l84Ym/lz7UXn2kd2w4eCmqKKYm/jM45C24VDdG1r5/cmsjtzHAfQ1OCEOIM3eXveBnquV4nrNUNbvbGGKvL89svx1A0dsDGeaPOcMfd+xLe0QXU9U3t+S1ay2OJDDx89D+H1kULPKI/Ur/znRN2XB1ipuMJngJ4sIzkUV7PfxE8rJcG8STdRfE8cFTk8E8H+Om0F1F5LfQKjV7JIiuTDeSEWmChayUTvPvYPY/4rwAP4l7KE6/hZ6SP9p3aPwEeJIjmBxmqek6QsRbONxJJKetmfHKEwb4+RudmqHweQICpMufVTXDw88PHzw9fW220FM5w5KQGpt5+ePv54SdMl4IJjU4lb1DMkmDYIZ5laqSXjsYmmhobaKpJIcrXDnfPG4S+rqSxqYkm2dRKZ8cXmpuqSf/QwPhcJ5+iXLns4Y9nxCsqH5mjqO7P/Y9N9G6WAQnSlXEG26opy37FizuXcFU7xG+/nZBFpHEJSyIx/SMtj0xQOO9EUFIepb0Da6FP+3sY6Egi3EYTMzN/gtI61tbLwtkPMjQ2xeTCjlruvwQe4l3O87OcNlbuPvgGBBDwA5O/lyWm6opc/I6rhXSll/oPT7l/9TphuUOMLYiYK4vhoZsNqn9SXFK61ENHUQwe8sdQOy/HIXlrXO5lUS+MYE++5YbmWZyub4CHYAx+PYnppUjCHyeQcP8aNx11+P2gDq53YniYkMjTCH+uWGnz21aLB+kyC6NN1CQF4qavjr51ACEvS6nr7qQ97QpuugoonFNBRccYK1tLjI0tsLaxwtJIDRUlHTStb/Oud4SajEhCHAwwMvfC72k5fctfB6y21+t/49I3wINIBhTMsHKL5FmLoOX1R8DDWl0z1faRD1FuqCq5cO9jC419I4wNDzM40M3cVDZ3LdXRUdVD38p17Z319cZS+Qgnzqy134T3W/Yu+/lx+dZTkj81MypoDkiXGW/M4X1cCEGe9tiYm2Jo4YbfzWievcslNz+brBfhBFrrYGjmTmB8Lvlf2ulpKODVw0dk1nVRX5NP7rOb+DsYo62hi7G1O5fupZJZM8CcRIR0dZyBjjZahTqhvpCCd3fxVFfFNSKN1/m16/VFM01N7fT11FD8uZbK+mb62z/yzPw4plff8PzhTSIvOaEps1rYqT8jRioepyjUCHNdS2xD3lI5KVjzSpFMl/L6uh0ONj5cSlsTSZf+l8DDmg2cmNWFUYarhYgj7jgaaKBj4Yvvs1qZoPaO2ugPFdDvgwfhdFIxy52J3HPS4KycBmpuz6iYFrEgOHAIZWu2g47cCBxO/4aK23NeVQ4yO7+fxsOPpu8/jWqx9ToS5ge/bNN4mJCImOmr45OfHIoX1VDXM8XGwgAjTVV+O2DHg++ChzW40RjnhJuNB573P9K660n8AHiQztNV9IJIBzWOOLyiom/qh6IxSBa+kBXhhJ2eGYY3PspMbpeHs3keYIW5iSc+L1uZFXyfN1rZW7PjD83/HeBhkKHGeHyP/YszVtEklvTuMcooZbE9n+JQfX7+91nckhuo2kPI68dvRcJ8RwGFt3U5+K/fMYsqo6h7G3rcfqpN8KCMjU8scWUTzMzMyKbJ+jginM0w2wIehINFY5UUP/XD8qIiZ91ek9s6waxIimTiC+3v/FE+cAGfpAaqRiRIZ7sZKHmIt94FFE/KcfzEcY4dPcyRo2c47fmOwo4pFvYxGRau9Y8AD5JFxhoySbtqzGmTR7x7foXL9lYYOkWS1DbKeEkEdx5lk12cS8HrYJwuqmHxtJuW0QV6N8GDYPGwwmDxXW7ZW2LsEsXrAWHUcvvjWFuSstD0gey373mV38LA9DgTeRHc8nXC2kro/AmTBVaWOlyU+40jcmc5o26E5eY2K6xsArjxOJuqpW/rAux1dfingAcpq2OlFMS5oysnz1m/D3zumtrdufkmeBDY7BSr9ffw0VVE3yWS6M87XDX2zoT1RyH0kltJ9dfHwsoPv4SGPd7hb51g722rNY+556yFkp4n1/KXWNxqVy87RKACw+TcMsFCzRDTwNeyzt+uqnfv0++xVmhMDfAxxAyLC+rouseSO7e3INLawRsaDxYcV/InoXOIrimhXpiiv+YDmVc0ZFEt4mTgQThCgmhhmK7XXjhqqmDoEcWT8lGWZGJNq3Sk+OEngIfA19SJBIsHMUOFj3jgpYuy/HGOHpXj9MnD/HrgBJoeD4mvntr0/9zjZv4B4EGKZGWamcpIrljZ4xNym7svwnCXP4XBnWoqm6ooSksmNvoVJX3tZFxWRM/6OuFvvzA+uR08FHc20ZAViuPZsxhF1lDZt7g3EJIuy3zbi19E8Sy/ne7xUdqKU3h6ZYt2k5UVlsaaaF04zoGfD3FG0wRDs406wworOyeZJVxV9+Q3wcPK7CBF15XRuWCKS1QWVXvp7yz3M9l4H+dTh1GyiyG2cG+wvvfz+6vWCmFFKyhL8ETjwK/8ejGA6LwW+pfWkZp4GVF/OtFuuhgYe+L1tJax1SVEY2+4a6uJpWM4UZ/3ELCQJU+onPexeBC1UfzMH3c9QwweNjM0u/qHLB4kojnGmj+S7q/GWRVPbr8ppKSuks8ZycTdf0x64zi1uS95GeGHq4sLLuuTs6kaKvInOXRcCV0nFxw3trn6EXQniex+iQxiSheaqc6KJdzbAw93F1ws1VA8dQL5C1ro2Xng6emBp7srLi5+hMRnUyBQSNlPzODHcMK8HVDRdeaW4xnOOMbztnpwc/QV6SqSiQJeBttionCCI4cOcfi3A/z7tBO33tbQOrvKfG81ZSHqnNW8SnTeF3oXl1gURG7np1kYe8d9J10srYMJyRnbjH62uCSI8u6BQv9L4OGppza/a4eTVtdJ1+gYY2Pfn0aaU3kRbIvhdyweRCN5vAt3wkTNEvd34wzPidfBgxXKRrdIGZ1haHqt3TUz+YW63A1xyWJyG8fW2mSzgqbZAksykWBBZ7ybrpIXXNa3IzzAGA09N1z3BQ9/UlxSPM5g/Rsi9E6jYXqFexkNtE+LWB7vpPPlJXxcnXBw9cDOXBdtAUafdyUkIYOcnHhirrlgYejBjeJ5xuen6Mi8S5iLLUbOD8galTD35z9m62X1D/7tBx4Efa36h9ywNsPC8yHJXX8QPAhCrtONlCQE4aGjjtLlTIrq6qguSuf1iySepnyic7GVjIfXuOEtvHPr77OzI0YXDiF38hwKmmY4ydY74+zsgmtADE+zvzAiEcL4TlAWZYujvjrq5t4EPn5PUdsgnfV5ZDy5hp+lNioKSlwwusL93BZahkboq0rj5VVjzhxTwfFxGWVd48xP9tJZk0PawwA8TVRRNwkgOKGCodU5xJOFvAwP4qqXB26O5ljonufQL0dQNLTF2tkDTw933N08cHYJJLZkgC5ZpCQpS6OtNEfroW3sh6O5Dg6ODhiH5stE9DdqFNlTEgb2V7vIum6GpaYW+m4RxBZ0MDa3wER5JNdtTbFwv09C23qh+K+Chy3laGmQltznJD1NIK5wWPaN3LOJjhSxEDhiYZ65hSWEWA977fcD4GHND6f5uQtOOqc5+OsRTimaYvu8ne6plbWGgHRVFj+68o4eGheM8X6Sz+feZtrzQzD9+VdsHtTyufePvk1/J3hoYmR5keHWCvL8jqNk6IpncBRPY0II87Xi5K92RH/X1WINPDQ8ssHe1BHnux9kImdbHpUsFMt3LR6k83TkxxNheZ6frJIp7Z38DngQHqOY8aJwgq200LW+xp2SyTUTLfEsHZl3uO1giIpZCK96xUxtd9rbnrwfWvobwINkgtHWt9xW+onTRrd5nNfOzK7SKWaq8QPv/dX46SdtAjNbady90w/dwcZO4tFamlPcOf/Tz2gHZ5PdPLt3Q1Y4YAt4sLucQGL9CoL5pcwEsz2R+26WmO8AD0iWmWrNJee2CScOq+AWX075wBLi8Qba0y5x8YAifi8bqBYAysoQ463pRF66QdSTNLJK0nn1xB87hQsoXi+jbmD+m75U/wTwIJmspjThKrZqhji97OLLhwhCHNbAQ0rfKqvLM8zMLTHd9J70206oXnQgrHqWwYVV+raBh1VmRrNJCBT847y4kjXO8i5iJjS85qh54oqHtSsudz7QuipBtDzH7MwUUxtRaSYGmRzJ4b6NKkaWVwhMrmdsY5vwPzXD7PzSphvMRtn4sf9/CnhYoj8/hvt2SsidMScwf4zumT1CjH4HPCBdQrpYzlN3LfQNPfCKq2Jqnw/Frvz5myweRB0veeKjj4aWI95vhWgQOyqGdQ0KAXgIIWxtwnLo+aaFwq6U71ghnH+Z5hdeeOmooW4bwcsBCct7tPPXDtwAD5acULnKq+E5RoUGr0TEVFshucFr4TS/ggeBPawgmm/hQ4g1trqmmAW+pHRKwqp0lbZkX3yt7TC7msoXGXiAudo03sZHcvNBMm9ySmhK9cFaXQvzK0I4TcE39lu//7GrhXSRxYka3ngqY+xwm8h37ynMjtgED1U9M7KO1txUHxM97wjROI6RoKRePiqLYLPV1aJ4YIr+6lSiLU5xyjqO9PqhPawKhT5wN635D/BVPY/erSLKu2ZYWVlkYWZqS7SqSca/pJMRbsc5OR0uvWulumfi6/apKabm1nRl9s9fCavzI3x5YIjxRQNsQ1Mp2GVmD9K5bsaKb2B24jCa3okkV07u/5351qP8j7YtM1yeyFPPi/wqaGQdkEMnQNBymVgHK1IQz9OVfpNgG0O0bG6T2DnP3PB/CB5W68l77IOzlik2CQOMzosRRukK4vxw0zPF+kU/I3MipOIeypOC8DXURzOshpEZYRBAxHR7Lpl3rFA98ivHlfTRVFdBVeE0J+WVUTYNIr56nL6pBeamJ2Ud4OnpaYRp7FMkEU5WqJqE8HJgiqHJSUaGBe2QcabmFmXWDrLnuthKXc4z7l3yxNvDCh35Q5w6r4WWuQtePr74+nrj62yA+nkNLK885Y3Q2Vr/iYZzSYtwQuvwYU6clMf4QTXlvVthmBhWGslPiuXJ/afEPo0nwfsCJzWDeJTfypBYxER7MW/dT3La+A4v3mdTVpJFSkICCS+ekRAfgIOmAiqqZlgFxZEgrE9IIDm7mrqe6d3tgf8SeIh3PMW/fj7CcfmznN0r4tVe684c59ihI5xW/Za4pITJkmjuu5mjZnKDxAGRbKBmXrB4cNVB7pQWVgFB+F8NIigoiKBALzwc9Ljwy8+c0XPGwesKV4X1QbcIDnlGdscUY8tCu1zE0vwUPc1NtCW54WDmvg94qKEx4waGh1QJeN9F4+QfiWoh6DhMMFxVSHXXCENzq2uuPEKkuEVBc2CKqekx6jMeEmZxnoO6kXxoG2Z8YZCeljrKCuvpXZDKQqaL5vvp7Wyl9suwzE1930/PRkH8q/+/AR5WysK5YmGJ5aV43gvt1h+2eJBXBFHJAAAgAElEQVTCaj+1L6/jb6CA3FHBOtAY9YtnOHNKnnPabng8+ES3SMTs3AST4xOMCwKq05NMTvTz/ooqBlY++MUVMzQ1ydTkEINDY4xNzLGwtLr2LkjFLPbU0dTSRlPfBFMj3UyUhOGhp4SyiiFm3nd5lF5J2+gMM1M91GVEEeKgh6qyCVYPSqgemGGi7QPvH17FySWUZ/Xj9PR30NE1QP/IPGJBNH2qhDf3bnLDzwVbE3WUThzjiJoNdq5eePv64utpj6OZFicPaRKY0S4rQ8LjkS6PstT2GD8dJS4cPoSSZQBBWULgg50d8bXyujhSTd7jS3joK3NOxQz7u6+466GJoaEzHvdzad8A3P8r8CAVyyIgLgiQdHkbOtlRGhcZqssi41kM9+KyqZmUsLjH7t8BD1LEC6NM18Vzw1wDM9cgvAICuWWnwQmtAJ587qdPJpQhRbI8xVxnBslxaeRV9zA0+Q+weJCKWJ4bZ6S3mZrcl8TZH+fMBV30zW3xuH6PwMhkcgSNB8d7PMmopqcpj6Knl7nwqx2PfhQ8xFhjb+KA053/BDzEEW6hwL8tk74PHkTzSMZLSPDTx0jfEbd7H6if2niyEpaG14RMHLV00bv+gc+9M8xvbN5RRH5s8W8AD4KIVG8pKTa/o6DiScjbarq/ft/XkiVdpL/sJY9tzvDvI848KOuh5xsGCj9yL5LRWlqSXTn708/o3frEx9adMdi3nGUrePBPIGmLM6+4I4kH7nuAB4H2LQwxUPWaCHMdfB59oqBzhpXRWtpeenPuFxX83zSuRRAQ/FgXRulp7aJvcJyp6QZKU2/gcl6RizcraRjcK9bv1/T9E8CDdK6eqsxYAjwe8LF/kbHPUdzeAA/9YsRCyD/JIt05UdxxMeWi1QMKxleYFYt2gAcRc8s9FD/xw8vMAsPbJYwIYeC+3i6IF5BOFfDERRNT6yCuvW6RwapdnYT/PxCXXB34xJsQWwyUNFB0iqdkeHnvUZLvgQfh8y2dpDrGCWcDE4wvJVA8Ld3H2mTrwxC+nn+PxYN0Kp/Xt6wxVDXD5H49k0vi7Z02ySLSmRweOqijqeOBZ1y1TJR3VznYkdz9F4UjV2l87oGHtjqa9vdIHf0R8GDFCZUgXo8syMwOBcuGmfYi8q7vAR5krn9LjNe/JO5mAD5XYsnsXWFFvELTC0+8rOwwD05HsGQVyrx4doSRgV66+kcZGx1mviAIZ20dLK4mkNr9vcr8fwweWGZxqo2MK3bcSc4nv6Oe+twt4EFmtSBFNNlKb9YVDE+p4v6ogML+FSTTOywehlaY6y6l5LYep8+7c/djE51LO5+0lKWuPHIjnVA+pYff+y5aJ/eGM/+5uKQggzDF+DtPHDTUMfKL5+XGSNRmAZMiGm2kO9kR5aMnMAn9QFbbX+X2uHmR78xIWez9RHqEO7aGppjceMYDdw0MDBzwepBN2ehGzSpleaSK0ux3JL8uoXVmkZXRPwMecmSd6vnFZVZH80gLd8Jc2x7f7DkmFwW3wh8FD6sMlSXx1F2RI0fkuahnjY2bP0ERj3mamkdxdSt90yssLQ/QmJfM0zv3eVw6wfSyhJUt4pLp04sMV73gUVgUMa8/0zS/pcyI55kZ66ejMpeieFd0zqhjfyuBpOIvtDQ30lhbSGqQHgYGLvg+yqNx67GrQ9Sm3uay+kkOnrIlrHSQrm1CcUInYp5JQfixb5DOukLygpSQ177Ok8I2RsSzDDWkE218FEX3l6S/iuVluDsGmnoYW1ljbWWErroyqmpaaBtbYW1tiYmOGno+j3lWsIdOyC7wsMjq6grLi0sy67evd/1nNR6kLAy301KYSlLiC5Ke+GGvpYCKhhV2txJJTk7eNr2IieCe4wVOyOlgFXCbe4kppGYWUjko2UOrR4isME5VvA8+llYYXkmTuVgKnHkNPGhy+Kgi2rYO2Ng74ODggIO9OWb6SmvikkpGGJjbra13cMfZ4w6vG8c3w1IKrnUrC9OMpHrhYr4PeCiqpP7NJdR/OYSing2Wjm642RpjpibPLz/Jo2Flj52bG86WBhiqKPHrpquFlJXZMYZbq6koyqe4rJLyqmqqq3dOFWQ8CcbfQJ4DqoE8ySqguKqM0pIiCj4VU7ZxTFUZZaWlFJfWUtc+wsyKYPf2X/ztBA9lE4hESywsLDH81hd3Ixtsr7+iRAje8kfAw8oXMsOdsT5/ipMXdDBx8MTrajj3n78lo6ieL93jLEgkLHdn8CrmEY+SCqidFiwZtohLJlUwOvyFkoceBEZlkd80KhOZ3cgd6coCS8srLAui56JZVkeKeJ/0hndZxVQ0ttLR2URTSSrPbgnP0QxL56vcepFPTf8Mk/2lZN33wNncBlP/V9TMClZRq6yuiliVCcKIYHWS4e4OGnNiiPKxRF3NloD0OkrrmmlprqM4NYoIJy0OqV0npX6Y0Y2BEsFtbaGF176a6JxRxsAvlrf9e3+XZPcinmdq4AsV72O4522KupIKiscPo+p0l/u5fV+tqv5X4GEjw7/3L2rm06NLuKkromLoxrVP8wzP7UZp3wAPQidqhKHat7y4rIO2njvXnhdSVFVCWYI/xvLnsAjP5lP7FLK8lgoUe5Kh/nGmZpdYXfgfggfxIpKZOvJTYnkUdoPgAB887M0xUviV3+UUUdGzwPnaA0Ji08iRWTy44XMzmsS4cO5esubkATuif9DVouG/CR4kC8wP11L6xBVLNU0sL8eSWLHF1E8oFOIZ2TNLvmqOwkVLLj/O5XPXJNu+j98rPNu2/w3gATFLY200RGqjeUEf56hM8oe2RykQNBNq00LxVJHnN71oMltHmRbKr1SEaHmWsYFhphdWWdldprelfnNBEBxrzeXjTX0O/3Qax/gqyga/URH8KfCw1iFbnh2iNTeN/JpOuieWWB2qpuWFGyd/0SQwvZmGPUbJBLP16rf/b4EHVkcZ6mqkKL+ZCZGExcoHO8ADSGcb+HjfAxdTa8zvlDK4ImaV3eBhXrJIV9ZdIpxMULSNpXxGtKXMrgHQiYJruGprYnnlGS8b/+HhNAXBpVWhETrIxOwSS3v7jmwWzx+fkTJV9Zg7rkZo6Htx5W0bEyuSvRsq3wUPwlUlTHyOJNTBFAPb2zxr/8F36m8CD6x28fmpP+7aWii6JFA5vrwNnEqWJ5mpCJO5h8giLnwc2rx3yfI005OTjE8t7nY72S+DpatIlzrJumWOuaoBgmZE2fy34MuGxcMfAQ9rFxdUvttrSikqrKF1UoRYskJdnAvuVnZY3sqiU4hkuiOdW6Na/L8BHgQrpGk6CrKp7x5heL6Dprzt4EEqXWSyNY/MQC3klb2J+thC16Lg07oTPIgRTbbQm3EJnRPqeMeX8Hl4o9O8nlHSWbrzYohy0uW0zk1S22cY32j87cjLvwI8CCHWVppiuW6hjq5VMLcy+1gU+psb1xLE8to+kXVZmRNyuvgmVVC1Gap2bSfpyiwzkxOMjs9uRmrYOPw//ZcKITVH68iP9cPLxgZz70ekfhmkuzhaFkXIzPkW4Tk9X9MsmZOlZXhohmXxEuI/7Gphj5H/c+pqM0nMrKel4DGx/tboGlwismGVWcFV6ofBg4SZ7lqq0uN4GJdAclom2UVV1LUPysTe1j71UlZ6c3gd6oG1mTe3Ps8wuQd4GP8ST4iDBfa+D3hWO7UdXkoXmOzIJ/OqNooX7QhKLKV2dAkhYknf5+eEWmtjGyCEQe3bJrYs6JZ8fhGI84Uj/HLMmIDMdlr2gVxCvTrbXUlhkDJn1sHD8GIvHUXR+J4/JoPr+W9jiPOz5OJFS3xik3iauLUjn0jC8xgCDI+jZBPCnYy23XXaNvBQyafyamoKcnmfWY7AJzelJ4QIYUNfaIs2QuGENVfTqmjYGEHdVeBESEQT9NSVUVFcyKfcHLIz3sqibKU+98dBSwFVTRscw1+Tmpq6bUqJj+ShywXkjulic/Uu0W/SeJueQVZuIfkFRVS3DTO8odMiFSOZLCHR3wwrC2+8E1tknSvhGcvAg5sRZ5QduPYmnVfp6aQL09s4nkS4oPf7r6i5RBERn7q2/n0WGR9KaRxd2AbgpaJFxtK89wUPrz4WU/3MjbO/nEbb2hlHT2+8HM2x1DzHgZ/PoWPnjJOXNx5CBDM15S3gQYhkV8THaF/cXF1x3XdywdpQHZWTv/Hvo2oY2TjguO++rrj5BOET9Ynm8fkt2j2ClsESU13VVHzK5H16Jh8Ka2gc+QvbFFvBg88DwpM+UfkxnfSGcYrvmGFt7IL7/VxkjPWPgAfxEIJIdWp8HM+S00jPLuRzXTs9o3PrYbIlSMWztL/yxsPGDc87GdTO7QQPlYwN1VN1RxcdPR9uv6mmbW6ztt1WeqWC+9hcB1/KSvmcn0P22xfE3wvG380RRztnvK8/JDaznNrOXoYbc3l73wcXcwssPe4Q86lTNpC18/sru4Bkgtb3Ydy0N0bN/BZJLfNMLK0w01tO3pOreFtYYhpZxpfRhbW+sKybMo9o/DOxzsqoHD+Hhkskz5u+YWEtu5CIxaEaKhIvYXTk/zj4738jp+3Bpdgi6ocX174x/3TwIO6l8lUENx0ssHS7xaPqRcYF+Lzjty94EC2M0lf9lpehjmieUcLiZjofm8dZFM0w0ZHPS28jnELfkdO4nUBtnH//qBYbe3zv/z9wtRBNIRp5S5SbJRa6uugZmWJsbIjB+YP8fs4C19DnpFc0UVtTLnO1UFTWQsfEFhcHcyz1NDgkaDxUFhB//yrhwXe4l7VXOE2htbHMfw08iBeZG6qjIjUcD5XTaFkEcz/rC11bifx6lopne+kpesJVYwXUDLwIiv9ERe8MG+6d38v57dv/DvAgRbIwylx5BF66Sug7hhCe/oWe8VkWlpdYmBmlvyaFZ9csUT+rhe7dShpHhDAugljbIIN1WbyIecLrojY6x5ZkjQqpaJmVkTbaO7vpGhhhbGqW2YUllleWWJidYLSnjqLk21w1VOB3BQ/uF3bTvUfebd77Jni4iJnjbcLSamhoaJBNdVlhBFoaYrzT1WLz4O0z4oEKGmMdOfKLPsHZrTTtVXH+vwgeEEzMxYgERy5h3HgXeBAxVRvLXTcTjC2vElE8JXOhkO4FHqQS5upTeHnVhguqfsR1LH2lx4KQ03AdBUGqaKo7cPlpIfWze398+EdYPEhZnR1ioDaLl08ekZhZRV3f9DZSv72E/JElCZNf3pIcFcHtB2/5PC7Z30Lhh8ADSEYLyYyN4GbIc9I7VmV+ed9N0d8FHqSL9BXFEeOmy1klJ0I/fKF+YIqZ+QUWhFGmlhJybuiho6SN1c3XZHUJZlBCXbzAUG02H16/JCWznMZJweJGuAsRc+OD9He00SGIWk1OMzu/yNLSIotzk0wMNNNcGE2giQqa+oLAVyXDgkXpvhmwDh78TTmm4Mq9ggqK6oV6oZ7SzOfEuavIwmluc7XY81xC4papjLbHxdIem7BcBva47v974EF4HGsq32KpEHmmcxd4EM1305R9Hz8NeS56vSKneWyt07ELPEiQLg8x2RyPz4UTWF5LJa1+9msnX+Ds0/XkRnvjoKOPVvAn2qaXNxt/O7P9rwAPgqWPdL6GtOtWWOpZYR6UwueuMcbnFllYmGVqsIHSNxH4qslxSvcm8aVdDG2yEqGczjJQl0t2SiIvXudR0jPP0i63sp0p/7FlyeoCC0MNlCdfxctEFyOHm4RntDEj+EOv9PA5LogbQeGEpDbJOtS7yvifEJf0tjJAycSDqCBLDALekBLpy3VHS7Tto8gcl6zFfv9h8ADCSLVELF4HcIJeyDyzYwP0trXQUNvKwPwELe9DueFghZ7zQ7KG10x5V7dYPLyfE7Ew/4XMUDucbbxwe1LF6FaoJ5lkuDGDOBcNtPTt8br1hIT0bLLTXxDnp4+Wvichb6ppGNvouguj8wuMVCXw2N8K9dNnUVVSROtSEm9rhpjcE3TtBg+Do9VUp/ijc/Qinq/aqc57RvwlZ3TNb/NmQrINsAoIcnVxkA/+ihi7fAc8nDqEwdUkou7c4KbPJdyCUqheYVvn9YfBg3QR0UITuTG3uB3gh69gTi5MPl542muhfEqOU+c00LX3+bptfR9vFzsctOQ4ePAsKsY2OMpcV9aP973MvTeVVPat1deCsO90xV2uWuhj5hZJXO1X69M18PDnxSU33pZN8GDqgM21F+QIUS0a4vBTlsc+OI2EtCzy79hyTM6GW2l55FRWU5UVT8JVK44esuTm21xyqqr5nPqQGC+jLeKSYsYaP5F51xkba2us950sMdK6yPljv/KvQxfQNDbDXLBs2W9/J1+cb+fwZXR+s60gWZ1jcbCMzMe3uBXgg5eXNz4BIYS++MyXkYVv6oBt5MN3/7eABxNLN6ydL3HVzZUrGTU8dFTB2PoqwSkNMjfMH7d4EK4qRSJec09e+0YLITzHGO5tpbmlnaaOPpYmqkjwUMfU9ho3XjcwLnv/t1g8JNcxNz/MTMUdPDQ1cbiVQlrz7C5AL5xfvDjFVGMGb57cJSxIiDJlg6WpOeYuV7kd/4GC+g66elr5UvRapj9ioWeAufsdojMb6NtlSbeRawI4FQRzr3PF2ggda3/Cn6WTVfCJtzGBXHG0wtg1ildtizLLK9lR0lWWJ9vpSL+Co5Y6amfPoG7qhlvMZ7rnJfsPlgpWD53F5D72Qf+0PNp6WqioaKFvdYnghGLq+2eYW2zgfZgDTia22N4vprOnh+6eHnp6uqjNiiXGVYOfLoaS1jIi05PYuItv/kuG+JJ5jytbolrsCWC+eZKNjatM9dRQnZ/N+9xauheke97v3uBBUAutT+XFVXPU5BU5a/WAd01jmyMJkqU5FlpLqOufYUyInbnHbw083PrfaDwIvssrbVTm5JCXV0plSw/tzVV8vnYOZfNQHucK4SbFzPYL4pInUDYP4NqDFLLfPuHZLWfOCBoPVRWkpT3nVcJrXhV/2SOc5hbwYGyLXUgqlZPrIjjrIoQzM5P0NxWTYneAc9qXCE8tp0MmRLZlv+kh6jIectP03D6uFlIkq0vMjzRQmhKCn+55Dp935HZWK22Tmy2aHU9Aimh+mPHi2/jqK6Ci54nf4wIaxwQBHumu0Iw7Dt6xKJFpLWQE6vHTMW/i6vsZ+POl8uu5pcuw2kpGsBnmqupo2lzlTmohVQ0NVOQl8fCSOUYXlVA0CeJp6wrjsopBwkzLR3KClPnl//5/HLJ6TFJpP4KthHh2mIn3wQRfvy0Li/Mqq4CiijoamxuoLHxHUoQXLvqKnD6ng/7tEmoH5/ZtoMoSuQkeFFA+p4a6if26WZ8D9ubqKMkroPlD4EHKas9nau5bc/CABWGF7bTvVcn9SfDgrqGC1ZUkUpr2KAtSoREnQiTab1qkIyOC23b6KJg/oGBqiamV/fbdsl4sEfoWu37bwcMKYskE5UJINH1zLINeUTbHetlbt3hw2QinKZL5bktGish/5IneOQO8MifoXnchki4PMlz7lEsKB1GyvU98YTdzMlXjtY/ahvaG7F88j2TpM08cNTC1vcGNd12sSCSI1/U5vu67Fldg1018c8WPajyImWzKJcPvLL//9H/8fM6Tm2+q6NnjEX3zcvtuFLMwJ2hbzO3xAd5y0EoF6eEOWGvbYPOsk5kVybZO2+aekhmZj+XI6Pw3Otybe6/NbAEP5n+huKRwcvFYNeWJ/pidPYW8vj8hzz/wqbySioLXpIQ7onf6KOcMrhOZ0ciwUBfJ0tLJ+6uGGJ04iLy+NzeLF9eEKaWzNOcmEn/Dj8DI57x8n0t+eS219TXUlGTyLjYYL115zsmrY3n9Jelt33ZxEho4szUppFzS5tffzqFlZYu1zBTYHhtTffQvnOTAuQC+Dx4EMY1Ziu5aYW/ljENUiaxht/NrugYegnD5g64WT9z1OaF+i7cj83s0QIQG4Zb3eWf9sDLKRE86oRrH0HaJIS6/l6Wd++y1LDQyd9YLu8DDAvPt2WSFW3P+lA5eaT20jq+9GLstHoQ8mmN+tIRYi5PoOt4l5mPXFg0kMTNVsUS56aOm605Q3pSs8ScV6r1d77uE1Z488h84c/64AVdzh2ibEu2x31oIuB2lfcuicIOrDORFEuagiaKyMVYhKaSX1FBVUUBWXDAB5sqcOKmG+cNKavq3vlOCe1IDr6+YoH/0J35TMME0upHhuR+MZLUlFbtmpSIWRxtpeHUZC4XjnNf2JjipbNvo4MpAMz1d3bRM7PMB/0PgYZHaeE88tY/w8+9ynDilhuXdVCJ8zHAQrNuuZ9ElREAWEvqj4EEqQby6zNLCHHMy3Z4xBprLKHwTwx1/X+wtrpJUl0uUvzmWJq44x1TIwoYL78w28CCLuCZm6GM4oc5W6DhF83FqHYII6ZGusDjRSUtuPFG+xmifPcaxY0c5euo0cr+fQjfkI3nNY8wLJu8yd8FlliZreXvTFlsDCwxdbpB8zxIVBX08Y3Io6J1nZRc82gAPSusWD610NmaRHWLN8VMuRNeO0lmZsAYeTG/xcniJyaUVVlY2pkXmp3t473cBo/3Ag6iHjpL7eMj9zEUtbRTOKKJm4I3/s0r6BbetzXfxD1g8SBdZXWgkOyqIG77rUV/cXHCxt0T//CGOHj7FGVUjrNy2RIRZjxrlam+JlepRfj0oz0V9C2y27ePF7ZdllMnCqogRL41SedcECx0bnO9kUjX/tUSvgQdrlI1DeTO5yNjCIouLiyzOttCYfxfnY4exuFtGQes0C8L6jWlpRSbCuXHbG+DB2UAPVWNXAiJCiQh1xfDUcSyDX/MwLpEEXwMOa90ls2dSVv+Ke4uoeOTBiWMePG0dYUAsZb4pi5xbVvy+6WoheByIWF0WREHnmd93mqbpQwwRVuc5qBNJVssAw3Pf2H9hkYWl9TInyw4pK+NtdCXboKdlh9etx8Q9f0iUvx1Kp8y4U9D77chsX7P023OSaVanMritdwalw7/x+wlVVM28ePThCS7K5zD2fEzc57G1tsYPWzwIMQlWWVlaYH5O0FgYY3ykk4aCtWgvXr7BeIcl0V0YjI3SRayuvSK1UYBPQpt1lvwbGpjYBRCYXMeidAnJcjOvvdUxs7vGzTeNMvfK7TclYXmik45kV+yMjDGx8ZJFf0rMrqZ9Yp6p6QmG20r59PwGl82UOX5cGdOgFN7XDzP1vXaZZJ6x1iI+xl/D10wJuV9+5pj8aY4eluO8vieXXrUwsbAsqwOkghbC4hA9lS8J1z2KknEQ14K8uepmirqeN3crxumfFZ7x9tRLxSssjtRQ8CwQJ5VzHNEK4lleDklRfrhpnee0ohm2j6toHKok9bYDtqrKKFtd4faW0LbBPvbYaSv+j8HD9vvab2kP8CAo7pfx5qYtZkraaLnEkNE5y4Tga7uRWUJlLFp7yTfX7biCdKGF9rybGP+H4pLtz51wMrDG9lYalT/s4y9AAREry8ssL6+wIlphZqBhW1SLreBBzfURSUWdzAxVUvcqCCVB46Gmh7bZBRYX5pkcaKIuTBk1u/s8y+9YN7/7Ch7sBKqpZYV74LoIjkzwRpgP5LKXM5YKP/H7CTV0rNzw3RDK2djnqj8etgZoXzi7B3gQMlzExJd0Eq/bYKqsiIKWGzdze+maXP72yKRgyrYyRUfWLa5YqHL+vD7Gl56S1bnIzP7KaTueorAoZrIhk3f+Wn8teJCNUopY6P5EargzlsonOXL4KHJycmvRHU4ooeUQysO8bmZWpesvqoSZ5hyyryqtgQfLGBI/98kaopL5UWYLQvE2UuSM3BEOHznK0WNyyB0XzneEo8fPcdHIC//YAhonl1kQfML2uNvNVVvAg+oFLbSt3DZDs7nZaqN69sIPgodVxoXID74aHDjmwaOqHvr2quhErVT9UVeLujDcNJT3AQ9SmcjRVH8bzc1CuJ+NMGFb/xvIiw3gsqkG8nrXSK7+QpUQZmzPfdfWN7e00dw9zuzyWqjQzfzaafHQO4d4rpRnHnrYOIcS+r7rq58aInpzorjrbIyClRDVYg08sNpB7ftQPJQVUQ+vXde5kLDYU0b5fQtOHzyL/aMSivuWEYtWWJmbZHx0lNGREUY2p15GBrKJtFbFyCKAKwk1DG1uW9tvdGyCiel5VoR6bOsNfHf+R8GDhNmuEgrD9ZD/9V/89IsubtE5lH/Lwua7196+g1TWIP5O6hdKSL1li7mGDXbPuvYHDzLLFQHOfOd8W5MgiDyuNvLKTw8zi78uqoXsEpJlZvuqKI33xkLxOKfljnH02DGOHRXe6dOc0vPnbmYjLRMra42hdfDwLtAQw+MHOa3nzfXCdfDAIl0F8TxwVuLUkUMc2agX5OSQO3aUY8dOclLFGu/obPJbx5n/2lrferdb5r+Ch4O/X0DPwQXH9Ya3s40JJsonOaDwA+BByL+lWpL99DE29cbraZ2sHtv5BP4MeBgqiuOxq94+4GFt9HZyoJuu1n3e9cZyKguecEnpCCqWNwlNLOTLN+qEtfqimfbeEUZ3SrLvAg8T9BTGEu1pgoLZA/IG5phe7wdLptsYzg5AR04Fz9jPFAuByxGzMtNHyQ019I18CUosp0+2v6AoOkRRlCMO2gboez+lZEYYTRKzPD/N1Pjoljph7b0X3EbTwwRxSV383jZR0SGID27UHcL+Y4xPC+E49+mYb5YCYXStn6bsGMLt1Th9+DDHjsnJOq9HjhzntIo5NmGZVAzNMbetPAk98U5y79hjc+5Xfjqmhrzne9om/6zY7WaCkC530ZofhfcFOU6p+RD+toamieUtnU+hIyBCLACjnXRr4zQCeBh5xR0bDSy+GdVCMB/oJSfUGgv5gxw+b4J1ZC51tQmE2mtjbHWV4Pd9X6Ov/CB4kC6N0NeQT+bzSMKuuOJgoo7yuZOcENTttexxuhLPq0fOWGhrYeLziMSmr5BwpZs+R5YAACAASURBVOIxD93sUbe4y/v1UO+rQzm8ue2Mma4zVz7NMLG0AV6lSMWLLE20UfXmDoHmSpw/fphfDx3jxO8H+O2sEXbBz0kt72Jkfp6ViTo+hFpgqqqLhX8crxsHmR4s4YWrKgZ6dnjc/0DZ0IZ1xGZmylwtCoLWwUNBJUVZD2XhMo+Zx1I8MMtUfSLxfraoaXoQnldOQWk55eUbUyklhR94YHsaTfu9LR6kc000ZgVj8uv/cfCXo1ywuU1kxhf659frxY2k/CFXCwG8rbI0P8usTLhzirH+JpqyQnFTPYqC3mWuvSijZ3JN1HND3FMQUxxp+Ux5mDZn5e0JSimiWiYW+HW/uUVB10YQNZ1mZSybeybn0LYKIeJ96zaXljXwYIi8ki2BiSm8eJlCSkoKKS+juR/ihM5vB1F1iiAkOmltvbDt1WtepZfxpX9y81yb4EFHgSMnznFRQwMNDVWUFVVwvvuMkNshXDHU4NzlPJpGF2SuKfuBh+wd4EHIWtk3WPgO7zut0PkpjkibCxzUf0he9wQz4m/tvxN6ipnuqiDL8wRmNzPIrB1hbn6cnuoMHhoewTm2htLe7S7Km4/8D8wI791SywPcleSQ+/0C2u5RxH4opiHOEs2z+njE5FI8st5o/VHwIBWz0F9DedYLnoQF4GtvjJ7SaU7LyXFaUQcjr5sEP4jhifUJzgn1VXodXeuDiwJ4+HRd/St4EIJLihfofeWFu5kDDrdSKd/DalgqE/acZGJ8gonJaWZmZ5ga66GzJIW46w5YaV1A4ZwqalZXCE2tp2VohoXVHRpSe+aboGE4zmBDDqkRbpjI/8Kho8c58vtvHJU7wxkDH4KfF1EzOMecEMGq6ClRjpocPWbI1bQGanvbqU2/R7DhRY7rBhJb3EvfhsuR7HpSVgY/k3HPDWs1JRS0PQkrHqF/epGFyXZq393jpo0e8pZx5LQUkRBij8Wpgxw8coqzZ89uTvInhDbSKf79P7V42DMDd63cAzwIpiUj1Gcm8+pZMqlV/TJTsp2EZtuZpAvMT7ZTlZVBzodscj5mk5lyn/uXDDj103EcnzZSOfgDH/OhGso/ppGUkEhiYgLJzx9w1fw8F5VtcbmXs0e4ym2p+MbC7nCa28CDeyxP3xXTUvSCpJv2HP/dicd1fTQPdsviqednJBBjfYxzNg9IKO5mUXalLeBB4wwnzmtguMuMygpLCzNMdNXR0NLHwMQcy11mVhYYaSlx/syZ7eBBPMfcYC1FT69x2UYHHU1DTNxCiUqvoXN6dQ/V/71uX8rKZCcNOU+I9LNET0UDDetAIrOaaRtb3rPTJZ37/9g7D+4ojqwN/6hvd+31rsNivE7rdcIZk3M22eScTEYkkYMIEiLnIECAyBICBTLKQijnNDPvd56GFs0wEsKAVxa3zmkmdVVXPdUz6L517637Kkg5ruitWxQZtU1RWzcofNYIDfrhv3rzq7namXr/UTK1UNd7/vcCtSXKT09WfOx+7dy0WsuXLlH4ms3atj9WcUlpyirx/rASapGt7CsHFLFitaJP3dDt/IdZpVEM64vuKvVyrI7tidKWdSu1fOlihS1ephVrtyp6/3Gdjr+h2zkPQ04a+9urYQQNwsPX6jV0oRbtu9ZgkCefWq9lU8Zo/Iz12nm7/rEgR2V/mSryr+nk1ghFRmzQ2uXzNHNkT3X++gu17btZx24VPFJrA/JVl6rw2iEd2bdT2zYt1OzRXfTdJ+304+Jkpd73ZstmX9RcpcTu14HoSEVGRSly02qtm9xR33z0tfrN2aUDT2Xn9Kn49jmdWPKLenXvpq5du4Y4uqj915/qkzbv6K33/qPvOndRp5DnPa7brfcv6jFtny6mFzlxwg28goWHrFr56gqVEX9KF+KvKyUrXxX5STq9a6f2bd+spRP6qtdPHfXlsK1Kqqx/2FagTHl3Lut41FbtSypUQSUZ0fN09/QGLez+ud5tN0UbzqUpu9qvquxruhY1RYP79VWf3r3Vu+Hoqd692uvLtu+ozfuf6tNvu6hXw2cPz+szcJzGzNmthMpHgod3EE0+96u6OFfpiXE6deGWskurPO6s3oqIPsUqvHVGp7aOVd8fh2vGhpO60tjudN6qv/d5oEJZV0/qyOYVCl+5SmvWrtby34ao3w//VbsOIzXhYMGjuMrfewGf8pJO6tT+KG2NjFJU5CZtWTVRA7/7RN/2malZe9OeTAb6ey/zqJ6/tlxludeVdPag9kau05oV4QpftVGbdh7TqYRbyiisdLbHe3g6v8WVyok/rEPbIhV14LySCnx6uAOdT1WFmUq7elonD2zX1vUrtWLZEi1ZslzL12zS5l1HdfJCkm5kF6mkOsTOIE+NwxtqMVLL4hJ0LhkDPkXxJ/do19zB6jIwXLuvZSk/6EemPjdRCcd3adOG9Vq/ZqkWTe6nzl98pp+HLVb4mQePvVf8Zcq9cVFx+7Ypast6bZjYQd85HhnbdYh4DE8JlGUp6+px7dy61fld2Ba1UUsnD1SfH77Xx93CdSK/Us4OX24dR6RJ1ZHwKZrYt7HfhY7q9PPX+uTtN/T2+5/pv992UJdn/C507dpdw+du1bb4kie9Zp4SHspVkX9HtxPP6WjcbRXWVCjz2imdObRdURsWK3x0e330zk8aF3lJl5zcCPzRV6H8s1Hafeis4m48DMtwci3citKiQT+oQ48JmhiZomLHrTdPl6IWaD7Jf4O+9727/KAfP/9A/3jzPX3yQzd16uH93eB5Xw1duFeHr+Y9nguXW/BjoE6VRVlKu3pGJ/ds0caVy7Q8fLXWbd2jA6cSlZxepHJ2O3miHvdplQrSEnVm00zNGNFfX02KUVpJ9Yt/d/xlenD7gg4sWaANR68p5X5FyGzij7tDXE+e7ibE6fSRwzpy9Jhiju7TnvCh6t3uc3UeGa71icHGtFvbL/mydGLZBM0YO0HTNh3VxetXdGJpf/X5qasGzorU4QxPQk1fmhIPrtLSKTM19+gDFVWxd/3Tu1r4iy/qRMR0Dfu5vX7u2kf9h47WhNnhWr3tkGLOX9SVhBhtHfODevSfptmRcUpOv6NrMft1+OBeRc0drEE/d9UPA8N1rEoPc1jUZiphx3zNGNBbXcOvKbO0VtUFt5V6Zre2LP9NU0f0U8+OHdWt/2hNCotQ1MGjit23TksmD9aAHt01YOJ8zdu4TTtn9Ve3735Qn4mrtOn0HeVV1TuLOrnnI7RkVC/16DpAw+Zv18mMWlXV1+rBzUu6dHyXtq6epymdPlCbjvMVsW+ropaPUv/23dVjZZLSSZKZHKmN4zvrw3c/1OftO+rnDh3VsaN7dFCHn3/Ul23fUtuec0PmePAXJerqnpnq9PGPGjgzQtvO3FBaYVWIe+k5PB7cKZZfFbkpSozZorVzx2pI1+/0bYdhmrYhVhfSykJeozo3SanhnfT5Z8M192Diw5wADe25TwKqL7mn+0cnq/cX7TRg3h7tv/lkviZHeBjxg9597xN93aWrOjf89nRUh5++1mdt2+rTr9vrp45dHv9N062Hug8OV/Sle06oHFdzhYfh3fup57hlijrnijqXdCkmQuGTBqnrz4M09nCecivqnb+HG4SH90dqw+mj2nvigLaETdKkXj/onS9nadedgqd+192RPf1Yp3uxGxuEh5PpxSFW6p+u9fgdn8oyrihu2n/VfsQ6RZ5L14OiNN04vkYTvvpYo6NSFf84jutxted85uzAcHudJvcdqklzt2h3bJwuxazXbx3/rS+6zdPa47d03w0nYkX/xg5tnD1Xc1cdVBy/0eSzuhWhhb/0VN/hS7Xxer0C/loVnAjT/FG91aFDN/XoP0wjxs/UgrXbtTvmnC5dOK4TUXM18D//Uf9FRxVzJVk3k8/p2P7d2r97vaZ2+1jtek3XbztSHnq4BXyqvrNNy0cN0rBxKxR5M3gVGnGiWjXFaboZf1JHd27QmkXTNXnEAPXq2F6deg7UsKmLtWLbUZ2+ypbL1aqpd4XI0MAI82LR68TOtVo+a6xG9u+prp17qsfwWVoSdVgHD+3WjrVzNGVob3Vs31WjVu3SuvB5mje0uzp07K8h4Sd1KbNM5XU1Ks9K0MWoWRr07Rfq2H+aluxLVGphvchLVXVrn9ZN6aeeP7ZX54HTtWB3gtLKfA9FOn+NKvPv6U7iWcWcS9P94kTtWTBYgzv1UI9ZO3T67FnFnT2rs2fP6FDEQs3t/92f1eOBSfCp7H6W7ufkPtyeJvS8PH7XX6LC9NPaNmmwxgwbqiFDh2pw/x7q2fFnfd1pgtaczVZ6CIXqcQOOfChfxjHtXjlL44cN07DhwzRiaH/16NRZfcat0Prjtx7GGD1RqbkvHv6IXvjtS/3Qb6HWn7z1MNQiO0mxkz7VT2M2aO36CB1cPVljhw5Wp8Hrdexevu4mxehYxGyNHz1c/X9qp95zD+p4asGjH92HwkPqplEa26+veo+aq9W79mjPnuc9orVu0SSN6vmzPh6xW/FZJc5KAapw0d2Tivq1vXr3Ha4x8yO0/XSq7hZ5DfHmjD+g2uJ03T63U5vnj1ffjv00Z2+Skh6Ebsdfek9Z5zZo2rChGsn+ucN+0cA+PdW9x2ANXHRSibllTxmczelF0+cEVF9DLGeOMtLSnF0eiipqQm95GKhTfXWJ8jJzVFRRq6d2diG+itjt+1nKSLune/fSlZlbqJKK2qY9RII76KtQoPiMdoTN1droOJ19uMzmnBUov6cbl8/q7Plk3SoO+vHyl6o055x2zhqlyaOGafCA3urTq496D5up+ftvK62o5tEftQH5KvJVeHKJ5k8ZpaEDe6lX967q3GeC5p/MU3bZw/8IH16wzvlD72zEXM0fP1zDhg/XsKGD9Uv39urUa5LCdl9WyhPWBbX8KstKUnz0PM2cNlVTp0zRlJdwTJ05T9PXnlHK/bKnjO66+FUKGz5QPUeu0M7shzH2gbpq1dbWqaa2SCWZsdoydYymjB6qgb17qNcvkzVxU4Lyav2PvlNkoi5TYW6uinnP+Ypl6dbpCIUN66c+c2N1ObNU1YGAqu9f1/U9CzRhDPuxP+32+WtT742brZnLDyup6pHgETz3L+O1r1rVD1Kc5FXDRi7T2sMpyn2W9voi1w1U6t6ZbYqYNlB9+vZT/wH91btLB3Xq0l/D52zRgTvPef8/0RfW4euVfXqjNs4b6+ynPXz4UA0b0E1duvTTr2G7dCClNMjIeqKB3/mC1fkqlRfmKjczUxnZeXpQyipuaE8Vf1WxigoK9KAIb5agS7IagtB3P0tZ6WlKS8tQRk6+Csoaby+ohUcvA6pOO6/z0as0de5OXSypUZljXZLXI0/3r53UoZhE3c4vf+p3sj7rtI5vXqBJI4Zq6C/91adrF/X8ZYoWbItTfJ4n0W2gTHfjdmjzzGEa/MtA9e+CcT1R87dfUErQ/6P+0hu6cWKdZg4fqhHDh2vE8MHq36u7evQdpZEr4nSnvDboe4qIeU8Xdq7Ryt+mvpTfhIe/K9O0aPNRxdyoeFLQrr+n6yeXaswXn6vHskQlZFYpEGDlvVaVVXVO4rS7xzdo47wxGjF0kH7p2UUd+83X5nP3lOGGpJFHpjhXD4rKG8ShQF2FKpI2aenYEZqwIEq7UssdN10FCnR170qtmD6qwUOtyd+CJ34nRmnS6qM6mVLwbOHBvRt8daqrKFReVroyMrOVW1imiqduPu+dxGLAbV2OXqSFE8dpZPQd5Vc+7T3mrdG8537VVpTowe2byimvc7aPbLoe3hdpOrtlkeaPHqLBQ4Zq6NBfNKDL9/qxwy8av/KIzpLsJGThh7lcaZdP68K5eCVm5qmyMEG7xnbS4FELtexQirLZ1tAtgVLlpyXp6vmLupxR5WSglyM8zNSEbl3UftEVPSitVaDytpLP7NXmFesUEb1PBwmTTc1QdlGlalhYqMrU1X3rFL3/vM7fylFe+mUdXTxGk8aO1JDeXdSj71iNC49xDF5yWpKDK//mBcXti9bmM5kqqq5XTc55xW5doIlDBmvw8LGaNHel1u8+rYs37zvu0vXlObobH6MDm1dpY1SU1kdv07ph3TRk6iptPnVT99y/v8hfUpmplJj1WjZ5rEZNWK6d92pVUVerzDNRilw4RkMH9VOPDh3Ve84BnTx/WIc2zteUUXO06mq5Smr9qkvZpk2T++rrr7ppyIJlWrRsuZYvd4+lWrp4jkb+/KG+HrhASw/d9iSLfAg2UJmlnJTj2rJuj2KTc5VX0VjIjis8dNeXHw3QjD3xSm40uaQ7aT6Vph7UvhWTNGzgYA36daYWbY3VxbuFjgeqe9bjx4AeCg+d9eXnIzSvCeGhtvCu0vdO1dB+kxR+8KquB+VrqriwVqt/7apPv+qjsWs2aNX6Ddqw4VlHhDZuO6WE9IKGrdlZjColnGXFJkUcTlRGw/+/AVXeOqDd4b9p9OT1isn1NXi6+XKvKHnXIvXuMV97z+3U+rULNGXYYA0eNFwDZu1XfF556C19H4PwPHtR4SGgupIMZR2bq4nDRmrclN80e+4MzRj/q/oO+k1R8feV/TK8KH2Vqi9O1KnDpxWfmqPcnFRdP7xY/b/uoLFrz+hCetnjv8UDAfmLbiv18mXFJ6Uph99oR3jYpEWDe6nfiGWKuF7veJyXJx3UgehNWh0RrZ2HTur0pRTdyilRWY1Pvqo8PUiJVdTydTqUlKes+ym6emyD5o8eoV9HDHS+NwNmRmr7JVcEDihQfVvxR/fq4MG4pwWXQLUqHqTq0uZZ+m3CSI0YNlRDh43QyFETNHHWEq2KOqTj8beVXlD5eCyemQr11F9dpKob27Vy2mj9OmS4fp0wS3NXbtPuUym6U1ClyqpSFWcmKyFmpzYtXaD1h2O0ceEMzRkzRuNXHNCZtHKVu+GtdWUqzXi44+CvfUdqflScLuf7FKgpUvWdnVo0YpBGTVioFfvjlRq8KBzwiaTBNbWIK3m6HrtLe6L3au+VQs/fXH5VZKfo2qEILVhzUskk8PT8BIcaX8N75Hg4vFTTOrXTB+NPKDnnGSG8DRV//5MQHg+/o7FAhUpyrujI4gmaNWm8xpFUZsJkTfltqZZEXdKNgsqn/gh7+ioB+Yuv6tz+LVoTtkiLFoVpUdhiLV2xWbvOpOpm8GQ83UAT7wRUU5SpWztnaE74Ph1PylF5wK+qwgwlR07RvIgTOrZ/l05sW6EFSyO0+cRtZZdVq+jmKcVGLtL0aTM1ffYyRZ1NV3qx+wciX7h65ZyNVPSmaO2ITW1QWpvoSIiPapR/+4JObgnX7K0JSiuqfGiEBapUnpeq06vnaeOec7qcXuzJ8h+imWe8FagrVWnmVcVs2qLYGw+U21hujuo8FdyM0ZbFYVrGPISFafGy1VobeUgn75Q7YRrNvZ+f0aWW/XGArXTylXXzuu5m5qugud/iANvJ3VXC7rXatHq5loev1OqIXdoTm6KsoEQrgepSVSfv1tZVYZo3Z67mL16jdTvPKKXYF7RahapcqhvHI7VtdVjDdyNscbjW7z6vK2lPex8A11dbpYqCbGVlZigj4yUdmdnKvF+mqhAuavV3j2n/+lVatvGIzhcFxXsHKlRRkKST65dp1dIwhS1bo427T+lcesUT7sDBN0Wgrlj5dy8p7uBu7U8sUQH7wjO26jKV5dxU8rVrunr16vMdSdeVeue+4/bY8LdI8IVf6LVf1YV3dTNmrRaNGqTJa0/q1K2SEKtEL3SRoMo+Fd06pzPblytsEb+fixS2eLlWbT6kY/EZTiz0i31v/SpNjdGxbaseth8WprDFS7UiYr9OXM1QnmskBvWqNb70VRSqIPOOkq9nqbCuieSeQYP3l95S8qk92royXMuXhTueXTtOJOt6bvlDsbnh/FrlpZzSsYj5mjNnrubOD9PK6DO6eKfgqT8myH+SlXREkYsXaTHzzrwsXaV1248p9nZZCM847oIalRfm6X5W5sv7XcjIVM6DYpUEZ7D25ykr+aA2zZihpYfSdLcgWPD2KS/xsA5vWa7FYUu0JHy9Nh1O1u38xxnCG7B4npDBvCo7QWcPHtGpyzeV5tx/jK1WJTl3dTf1d/wuXL2q5LuIW8F99Fz4hZ7iKfBAN2Mjte636Zo+d4P2p9W9xB1vnqdzDz0eEvev1epZD/9mGzd+giZMmq65q/brWGKWGnbpbqRZ18Vc/mrVldzS2Yjlio65qoQcj7dDI3Xlz9etM9HaHLZQc/feVUklxgpb2bFdZmO/ynjjsn1enWrr61Sed1PxUQu0eMEczZkXpvCtRxST9OBxiAfyO9vj1dQ0vBcoSdXVk7sVsRYD4ppu5VeG9ATzV1eourJIOZk3dT56q45fz9f9R//3eIcUqM7VnUundGzfccUXsw2vTwVJR3Vk82LNm7dQC5dv0p6EHOXkZ+nelQs6deScUqvlCKO+zDjFRq1X2Ip9ulAeCEr+7ZevtkiJUbO1bMM+HUzIffr/DwQ8YsPxIGnyxz2guqIM5RwJ07QJ4dp24Y4yGnNmaRhcQFVpcTqzb7NWrN+rw5fTlVdZ14TRhqCWrsxDCzVt6jrtir+n0E7OAdVXFuhB0mHt2nVaV9PyG0Ij3EvX3DqmoxtXas7yA4onIq3Jsbm1QjwG/KorSlfanTSl5RY/8dvJdr4p8ed19PRNR6hwJTZ/WbbuJ5/RjuhTSs24rBMHo7Vp9Tqt33ZIR689UGmIENMQV370Vr3uX4vR/mVTNHzeIV178LQY3XjdR58Q/lR+Q2ci5ui3MYM1sP9gDR03X4v3X1dGaSOLc89sNNQJD8M8uI98ZVnKSDislQu3KC6tSIWOiheqzqP3yKmTc1J7Vy3R8nUHdOLRIl2gtko1dfUhkwtSM+Dzqb6iQjU+7vVM3b64T5vmz9Fs/hZeskG74m7qzhN57HxODpS62hCxyoFKlWbHK2bBMI0dM16T5izXqsgDOno+Rbfz8I4MWiBsYjgNH9WVqT73lPZsjFDkjmM6nZTh/AY0fP7oiRPCVl6k8poi3Th3QmdOnNHFUL+Bvmr5y27qzM7dOnXltpN4kd9Of8UNnd57QHFX7iozxNaTT17Pp9qqClWQKyTod5L0ByzclbAtMnnYnqzY+Ct/kTISDihqwQyN3XRNGUVeb+vGq73IJy9HeHiRHlhdI2AEjECrIlCr3KQT2jV9oIYuPKS4e8V6IqSvVY3VBmMEjECzCJDXo/KUIudO1+x5W7QrMb/BIG5WfTvJCBiBPxWBgLPrV61q6/AADe2p17wB+VVfVe4ksyxv0qOqea21yrPYvaeyVBV1vufzbm6VMFr2oEx4aNnzY70zAkbgT0fA72S8LivMV3FZtWp/j9r+pxuzddgIGIGmCbCcWK6y4hIVk8Sy7jlWpZpu2D41AkagpRJwkk++hM41JLF8CW211iYIgcIZvbWOr5WMy4SHVjKRNgwjYARaDgHckP3Ps0tEy+m69cQIGIFXRcD5wzg4e/2rupi1awSMgBEwAkagZREw4aFlzYf1xggYASNgBIyAETACRsAIGAEjYASMQKsiYMJDq5pOG4wRMAJGwAgYASNgBIyAETACRsAIGIGWRcCEh5Y1H9YbI2AEjIARMAJGwAgYASNgBIyAETACrYqACQ+tajptMEbACBgBI2AEjIARMAJGwAgYASNgBFoWARMeWtZ8WG+MgBEwAkbACBgBI2AEjIARMAJGwAi0KgImPLSq6bTBGAEjYASMgBEwAkbACBgBI2AEjIARaFkETHhoWfNhvTECRsAIGAEjYASMgBEwAkbACBgBI9CqCJjw0Kqm0wZjBIyAETACRsAIGAEjYASMgBEwAkagZREw4aFlzYf1xggYASNgBIyAETACRsAIGAEjYASMQKsiYMJDq5pOG4wRMAJGwAgYASNgBIyAETACRsAIGIGWRcCEh5Y1H9YbI2AEjIARMAJGwAgYASNgBIyAETACrYqACQ+tajptMEbACBgBI2AEjIARMAJGwAgYASNgBFoWARMeWtZ8WG+MgBEwAkbACBgBI2AEjIARMAJGwAi0KgImPLSq6bTBGAEjYASMgBEwAkbACBgBI2AEjIARaFkETHhoWfNhvTECRsAIGAEjYASMgBEwAkbACBgBI9CqCJjw0Kqm0wZjBIyAETACRsAIGAEjYASMgBEwAkagZREw4aFlzYf1xggYASNgBIyAETACRsAIGAEjYASMQKsiYMJDq5pOG4wRMAJGwAgYASNgBIyAETACRsAIGIGWRcCEh5Y1H9YbI2AEjIARMAJGwAgYASNgBIyAETACrYqACQ+tajptMEbACBgBI2AEjIARMAJGwAgYASNgBFoWARMeWtZ8WG+MgBEwAkbACBgBI2AEjIARMAJGwAi0KgImPLSq6bTBGAEjYASMgBEwAkbACBgBI2AEjIARaFkETHhoWfNhvTECRsAIGAEjYASMgBEwAkbACBgBI9CqCJjw0Kqm0wZjBIyAETACRsAIGAEjYASMgBEwAkagZREw4aFlzYf1xggYASNgBIyAETACRsAIGAEjYASMQKsiYMJDq5pOG4wRMAJGwAgYASNgBIyAETACRsAIGIGWRcCEh5Y1H9YbI2AEjIARMAJGwAgYASNgBIyAETACrYqACQ+tajptMEbACBgBI2AEjIARMAJGwAgYASNgBFoWARMeWtZ8WG+MgBEwAkbACBgBI2AEjIARMAJGwAi0KgImPLSq6bTBGAEjYASMgBEwAkbACBgBI2AEjIARaFkETHhoWfNhvTECRsAIGAEjYASMgBEwAkbACBgBI9CqCJjw0Kqm0wZjBIyAETACRsAIGAEjYASMgBEwAkagZRFoscKDz+dTcXGxioqKVFhYqJKSElVVVbUseq+oN3V1daqtrRUMrBgBv9/v3A81NTWqr68Xr19GoS3uMw6eP2/h/rR79Hmp2flGwAgYASNgBIyAETACnofSMQAAIABJREFURuD1I9BihYf09HQNHjxYvXr1UqdOnTRixAjt3r271c8QhlxKSoouXLigzMzMVj9eG2DTBLgfEODOnz+vM2fOKDU11XnddK1nfxoIBJSWlqaLFy/q3Llzunnz5nOJCNTPysoS31MrRsAIGAEjYASMgBEwAkbACBiBpgi0WOEhPz9fa9eu1fDhw/XZZ5/p559/1ubNm5saS6v4DCNw4sSJjtgydepUscqNkWdFqqiocDxfYPI6FISB6Oho535YuHChVq1apZkzZ2rGjBnas2fP77o3qqurdfv2bS1ZskTcX2FhYVq+fLmmTZvmvL58+bLKy8tD4sXTgs+SkpK0ceNGjR8/Xhs2bAh5rr1pBIyAETACRsAIGAEjYASMgBFwCTRbeMD4/SNdqzEu79y5o8jISLVv317ff/+9IiIi3H632scDBw44osMbb7yhzp07O+ElL8u1/s8OLT4+XsePH1d2dnarF2MQ3jDq8fSZNWuWjh07prNnz2r79u2aMmWKBg0apKNHjzpCTHPnlfsIj4kFCxaoT58+WrlypWJiYnTq1Clt2rTJeQ8BAm8bwn28BY8LhA8Ei7lz5zpC4Mcff+wIId7znnoeqFZR2lUlnDyigzGXdaOwXlXPH9XxVLP2hhEwAkbACBgBI2AEjIARMAJ/HgJNCg/uCuetW7ccowej78SJE457NquxrJ6+ytV42k5MTFS/fv1eG+HhypUrjqHZu3dvx8Aj/v5VMv7z3KrSli1bNH/+fGfFvbUzQWggzGjgwIFPjJfv3MGDB/XTTz+pb9++jpDQ3PwMhGxs3bpVX3/9tSZNmuSEWrgcyaOCEPHtt986XhDBYT47d+7U0KFDHQ+k6dOn68MPP9Q777zzbOHBl6PEvYs1Z2A3de4/Tasvlyuv4uXkqPgz3bvWVyNgBIyAETACRsAIGAEj8DoTaFR4wCDBUMH1H5dqDI2//e1v+vvf/66vvvpK8+bN0/Xr119pwkf6cO3aNfXv3/+1ER4Yc25uru7du/dSYvlby82Ntw0hARjMuPq7BnNrGZ93HIxtwoQJ+u9//+uIUMFjZfxjx47VX//6V+3bt6/ZXg+EUYwZM0affvqp4zlSWlracFkELgTGH3/8UT179nTEjYYPJVVWVqqsrMxJREnC144dOzZPeKhL0rEVozXw0/f10dfdNHJ3vu4VWdJUL1t7bgSMgBEwAkbACBgBI2AEWjuBRoUH4ulJ5vjDDz/on//8p2Pk/N///Z/+8pe/OAIEq52suMbFxf2ujPjNAYvBhZH1OgkPcMHIfpm7FzSHdUs/JyMjQ6NGjXJCDFqz8ICXEUZ+ly5dHOFhxYoVT03N3bt3nXAJhMDZs2frxo0bT50T6g1ECsJ3vvjiC8dTAu8Jt7jeTSRy/eijj5yQCvczHvkucnBvPpfwEKhW/o3Tit25WWu2ntTVgnpVWqiFF609NwJGwAgYASNgBIyAETACrZ5Ao8ID8eS//vqr/vGPfwjBIdTx3nvvOa7vZMR/FaW5wgPnEfdPhn5CQfDS4HVwnDp95NwHDx7o6tWrzrlHjhxxwkgwbL2GmDseck0UFBQ4O0yQlA9PBNzSEWbYfQKjD88Q2kUs4Fyy/WMcci6x+hRWl6nPdXlka1CMPbfwnOvTN3YKgCnnuedg8LHinJOT47jI422Sl5fnrEBTj2vhHUJ/uCb9aazwGcYjK9z0h1watMGYyAGwf/9+52CcjbXj9pc+wI7+8kgbMKB/jCM4USHXwY2fPALkKCDHAOE0jNsdq7ffjJt+kFsATxtCUMg3wPaqMKU9zgkuzBusTp8+7azuX7p0ybknQl0juO7/8jX9Zg7IadKuXbuQyRvhR74F8oAMGzZMjK05hWStn3zyicMx1PcDjt27d9dbb72lyZMnh+TKOc8lPCig+qoSleTfV879ElXWB+Rv/NZszjDsHCNgBIyAETACRsAIGAEjYAT+ZARCCg8YZ8R74+odSnDwvsfK7N69e1/JsDF6m/J4wAjC8CXDP274a9ascQw1npOQj1h4whbcQnsYaeHh4Y5gQrJK8gYsXbpU7BpA4j4MeG/BoMbIYwcA3N85NyoqStu2bdOyZcucRH8k5uM8+rJo0SIRA8/OFBjLhw8fdoSNXbt2OYkyWcGeM2eO01euhYs7hVVu8jvQJtcivIUdBxBP6DdtI5KwqwHhBrjas4LNePBMoT8kIyQEhvEjvgSLBvBCJDl06JDWrVvn9GH9+vXOdWDBTgXMO6voGKAkHfS643u5IDAgGHCu21+ui/BDO7zPHDBurkmhLa4Na8ZG4lDO5TWsECLg4IoDCC0wWbx4sWOI43nz+eefO/PAexyxsbGOMOH2jTEj+nBPMlcwYWxcj/OZD8QK9xpuvZbyyJi5b7/88kt99913IXdyQTRg3hAe8FCA+bMK9xlJIRESyfGAOMX9EFwQdmiXpJYIDKHuoecTHoKvYK+NgBEwAkbACBgBI2AEjIAReN0IhBQeMHYxnHHl9ooMwc8Ju/jggw8cQ/dVgMPoaUp4QFTYsWOHs+sFbvgIAqygY1RjtGFEYei6q+4YWmTtJxs/hjUGOyvvGKZk+e/WrZtjjHO+a5hiXGNYI7Bg+OKmPmDAAMf4ZXeB999/38nwTz/wdBg9erRjDLZp08Y5F7ECMQGDnv4hPBCi0rZtW8eoZFWewjXPnDnjiAoYnW+++aYTb88KOBxoG3GBMVGXucE4pF0MatrmGDJkiJMgcNy4cY5x6U08iACAQU4MP9uUUhfjHzGD7Uq/+eYbJ5xh9erVznMEHTwRQhVEBLxiuB5MMGjxSIAvCSDJAfCvf/1Lv/zyi7OCzxhgyfmsunN92J88edIRS7p27erMAaE77nzh6cDniCzMJ+E95BphrvDG4WAO79+/39BFeDL/7PrANTDiMcwRHphzxs7uGAgczSkIPniG4N3yIgdeJcxFsCEf3AeMekSF//znP47YgjAWXBgjgg0CAewZ47MK84UXA99hhAfYhhIeYEu7JLVEwAnuL3VMeHgWbfvcCBgBI2AEjIARMAJGwAgYAS+BkMIDYQAYewgLwWJD8Ot3333XWeX3NvqynmP0NCY8II6w9SQGKcY/xiRGOgVXf8QEBAa8AwhBoC1WfREQMJIHDx7srKZzPp/h7YDxjXs7BjIM3MJz3kMQgAm7bCBwsNrO7gJs9+k1EBEQMHBxWSdZnzcBIMY/r+HIOXgmeAvjwmsCcYG6rvDAOfQTLwlyXsAd45RzGa9bWNFnNwQ8A9iFhBV0CkZvcnKyPvvsM+egDxiRtMk1EFNoD08FQiSio6Od0AnXI8Nt3/tIXdpA+MAApk9whRdeHQgZvCYEhnPZGYEcA8wLgowr7iBuIMgQugNbdkwJLnipIGww9sZyPNAe3idwo52EhATnurSFgMA14EqCRcJCmlPwwmD7STxQfs/BODnw6MD4d8fc2LURBPAcQZwhvwq7UAQXBDfubwQC8jEgED2r4OFA/13hAfEglPCAKIboxSP3S3B/TXh4Fmn73AgYASNgBIyAETACRsAIGIFgAiGFB4wLVq7ZwSJYaPC+xgjHWCXE4VUUjFWMH4xiYt4xttxC/oPffvvNMSSJcyfunfMpGO8YZxi+iAl4I1BcwxRvANrCEHQLIQt4TWDwsWKOAegWtz22GmQnAbxByDGAgY34gGGNYesWVsZZMWaFnpV3znMNOAx5chTAESGDbRO9hTEQ2gHXYOGB83Cz5/p4VGCEU99tm89pG08IjHvCDFxRgke8A/CU6NChg9Nv97pck9Vw6rBlInki6DP3gcvUPTfU4+bNmx0jmfHg3UF/MOzJr+AVhBAUCH/Ag4R+u21zPkII3jOIQsx5cHmW8EBfmU9EF9gQ7kJIhVv4nGtyTyBUIUZ5ubnnBT/iaUK4B94dTR0ISBzw58Cbwz24t7gnmuPxwDwhUjAXTQkPCCwID//+97+deQ3ud/Br7k/ue+47xDUEsFDjR7BxhQcEnuBz4Oh6PLz99tvP3k4zuCP22ggYASNgBIyAETACRsAIGIHXjkBI4QGDELd9VvO9QkOo57jSY3y/ikI/GvN4IN8BYQesktMHjGrc8jlwredzVv0REggpcAuCBK7zCBUYX+RmwGDEvR0PBFaQMY7dpJDUw2BkVR7hAeMYw9A1yjF2yXfAOW7BcKdPtIWA4y2cx7aGsMT7gH4GF4QcPgslPNB/N8SDFWw8MbwFEQQBhWsTMuGOg0e8OhCLyAtA/gZvoZ/UQSghMeTzFFbl6Svt4vbPvCHW4G0BG7cgBCBIIBphCMMUAQAPEcIzCM2gf66HiluPx2cJD1yHMA2MatpBfOE+cO8JHvEkwGsC8YV7FnHlWQVvEEI/mOfmHow7+CC0wxVamrpmsMeD15PGrecNtWDOXqbHgxtqgQCBWPLCwkOgSoV343XhyD7t3HdWqUX1qno6tYQ7NHs0AkbACBgBI2AEjIARMAJGoBUSCCk8ME6MZxIV4poeSnDAQMS9Hs8BjOFXUTDUMEJDbaeJsYsXBMIDeQMQDsiB4D0wzFnlJSTDLRiorGBzHgIDK/TUZayEYZBDgNAB12Cnnld4QMzg/KYKYgbCA+IBBrW30BZeAK7wQGhEcHmW8IDHAKv2rOozT97C6jp5JoKFB8aNBwL1WPVH8IAvB33CUEekITSlsbwO3ut4n7vCA94GiDiNGdi8j9GM6IE3BmEiiEKs3uNxwVzChZ02gtt4lvCAiMScwhxxCM8N773AczxJGB/3C4KN1yPCO57/5XPGQf4GPF64v0MJD97kkniZNCfHA0IIghV8yfGAZwXiWXBhDhFmEKAI6wmeh2CPhxkzZgQ38eTrutu6EL1Q0/t2V9d+4zXrYLoyimtlG1s8icleGQEjYASMgBEwAkbACBiB1kygUeGBFWtcxYlPx1DBKMQFm7wFGHa4gWM4YvgGr4q+LGAYPY15PGCsYkAjjGC0EovvurZ7HzHycZenj6xwswqOAdajRw/HuCLpIAkXEVAI6UB44D2v8Y1hzgo9Hg+sqHPtpoorPCBSwMhbMNzIPYABiHH5e4QHvBMYN0kh8d7wFrwpXOEBQ9sVUGCJIcm42a0Erw0MfOYPsYDEmhidhKU873y6wgN5AeDdWMHbgXNJ+ojIQ3JItnhk7hCBXJELUSC4D17hwfWIIGyFkBeMagx2Qj5gSpgCc+y9D4KfN5ZcMbjveCoQWgDHFzloI3hMwdfiNWNBSEBQ4H4Lda/hrYM3C6EW5MzAm+NZhXuYXS0In+L7zP3Me97CPUICToSHkSNHPnNXC5KtPlN4qL2q46snaPDnbfXBR1/o08mxupJRpqclD29P7LkRMAJGwAgYASNgBIyAETACrYlAo8IDg8QwYXWVVVdyFrACSyJFXPnxIsDYC14RfZlwXOEhVI4HDGRyFbCyHxwa4e0DY+BgdZsQAupg4CKosNOAWzBmyZ2A8IBYwMo8bvYIMNR3hQfEDgzcpgrCA0kVW5LwQH/Z/YEtLjH6MVgxLnmN4EDYCltfYiA/b+H+8CZ0DFUfoxvvBoQbGCKKMCeuMY4HBoIBggweKYhEfO7eXySGxGDG+8X1iGBOyLuAUEEYBAISzPF64FpuXW9/eA/BguuG+tx7Ls8RdjDs9+/f/7sPvit4m3DdZ12Te44QB75rhIWQbyS4uLkyEAgQb4LFHq7hHt66eNLAGFGDBKL0xy2cz2vuC8RFvGrcuXHP4dHr8YDwgNdNkyVQpuwrB7R99i/q89M3+mD0IV1KKzbhoUlo9qERMAJGwAgYASNgBIyAEWhdBJoUHhgqhgarvogMuGezUswqs9dofFVIMIYwKok3xxDzJpfEa4CdN0gkiBCCUBBcqI/hiMBAOAg7LZCMD+OVVXYEBbdwHUIz+HzhwoWOQYvBiSDBeax2s4NGc4QHBA5c1V+F8MA48fAgZOJ5PB4YJ7kVMMrJb4Bxz/hcTxHaxdgPZWy6jBp7ZEtO8oG4O0kEn+fyw6gleSScETiYH7fg+YHog/DA3NIvtkLFEKe4wgPXYK6oi7cGhjmCCQIRIhn9YHcNEo/yXnBhjBjq3MuhPg8+n7wReGUQMvN7D3bFIHwCb4Zn8eX7hojCdqSIBIhqwYV7mvEhPNB2sNcL1+HwCgu0wa4iJFvF4wWGsHCLO0cIc3iN4GESqjy38KCAfLVFuns6UutGdlb3VSlKvV8tf6jG7T0jYASMgBEwAkbACBgBI2AEWiWBZwoP/8tRBwsPGG9ucWPhMVYx0NhJwGtIcd7du3cdbw1W0wm3YBtFEg+y0s/53sLWkwgSCA/ke2CFGzd3jHMMVISL5oZapKamNkt4+PTTT5871AKBAM+M5ggPrPq7oRaMFU8MhBpyVCA8IMjACE+I3ys60K67q0VjoRYYwIRZIB4hPCCYYMC6BRGLZKastCM8EC5CKAHGL59REBjgT/JD8lhwbzAG3meueM118OBAXEGgcM9zr4NxzZgJ9YAFr59VENrwDkC0eZGD8XM9+tlU4XPYEMJADhVCRlzxxa2HMINHDbtKINggVFAYP+PCW4EcF8yL9zuBpwg5Lvi+BHu3cB68ENf4HvB9CFWeW3ioL1LWlX3aFjZdI0cuUFRqqfKrTXYIxdbeMwJGwAgYASNgBIyAETACrZVAixUeSIbIDg0kIWQVm1VYVvrdrSsxSPFGwMhi20oMqpiYGGe3CoxtVoFx53eNbFa4ES5IoMiqLt4TnIfBhYcChi67MmCYk3wQ8YH6GLWskLNrBkIBQgf9IGcAhpzXsMMzBKOZ0AOuQS4MjFzORfggxwKfYyxjYCOCkE+CdjBMOYdr4RGAsIDHBAYgxiTiAC72uO2z+wZGJ+EvJE0kDwbXhgdtk6+Btsn1gOhCTgCMVwxo6mK8k3Rz9uzZzjhZNSehJiv7bGvJdpbPWplHjCEPBvOBccz8kPcDd37e41r0iYKxilBEwk3CXHhknHyOYU+ySfJWwBcuCD7MJyEvrjiAAETfES+4J/CYwLDmXAQIt3BtBCbuGcJpMKYRawjLwGDnHkCcgPezRAC3zf/FI14phBgRAkNeEnjTX5hzf8GBbWS5z10Rh903mG9CUsjlAAfucXeceCoRokS4FHW577gvYMx8wYU5RPCBmbfwGs7kA+EeIVyDnC+IFISRwJ3vEfPsLYHyG0o4vEXrlq3Tmr2Jyqjyq7Zp7cVb3Z4bASNgBIyAETACRsAIGAEj0AoItFjhAUORBIis7nft2tUxlljlxfUcgwkjC6Of5xhYGGisEuP2j3EeHR3tGNa4ynMeBhar3RijuLFTh5wA5AjAeMfoxoAmHIDrcR6u6ceOHXPEC9ru0KGDY5ixmsxuGBixGM5uwSgkJIB2MP4x4lh5Zxx4WCCMcB3axjhkBR9xAOMZ4xujDsOdcWJY0k/CQxgPrvEYoCQIJEEkK9MICAgvGKIIKzzCi8+pj1HI+YwRQx3hAsEBjwBWvRE23AOjn/cRXxAkCGNwvQ3c8XkfEYYQPOCAUIMLP7kjEE14D2PU622B8cucIDqQUwJhB+OaMcGEfsKYPiDs8ByxxzWaEV4QR5gb8hpgYFOHR29eCoxoxBo4cE9wDtuIIiAhWCD0INB4BSPvuFrKc7wYmE8EAsaC8MI9hHDAnBPKA2NCKtzCnHAfuTlRCA3hvnAZch5eG9yPzDNiG6xoB48T7hvaRhzz1qEewgLzRF1EIu5N8nVwD3Id5pzvC+KatwTKbin18iWdvXhb2VUB283CC8eeGwEjYASMgBEwAkbACBiB14RAixUeMOIxjPBMwABjdRtDnxV9VuQxsiiszLNyjgGFMYuHAaICdTGCgt3UMWAx3hAHOBdDnJ0WMMoJp8B1nVAAXNHxQCAUAcOVPnAeB885MNa8K7wYefSXvnr7jNcFxhxGIcaZ2xaCA2PiWogDrM5jnFPfvQ7PGRtGPudQh894n2vRNv1DAMFQD742vGiXsWC8IkgghjBGVrYRSniEHYIHK9kk7IQPORMaK/BnhZv+uONx+0y/MJK9ogztIAogoDA35OVAsEDkQIBgvvHMQBhAYGCsXqOa+swRY0XcoD4r76G2csVoRrhClMBzwjXeEaVoI9iobmyM/+v3Eddgw1jx4MDYx+sDMQXBLdgrhXHhGYFYhlhFyE9w4RyEGuYKjxnaRIRAQOBeYA5cDwpvXbxFmOdQ8+3ei4g7eJJYMQJGwAgYASNgBIyAETACRsAIeAm0WOEBowrjFlHBe/Ce63buDgRjCoEBQxV3cg7Ow9ANNjIxqljtZkUZ0YBzaZ9z+YxVftrhHF7zPs+9fXCf877X+HNFEPdz95G+EH9PH0O1xXt8zhHqc+oxZj5323QfadvtR6i67rUJ98AAZ5UaEYTx85l7MGYMUhI6sqUiIREYrsH8vMxh4/Yj+JG+etm49RgH14Q7/Lku4+NceGNsc1A/+NreuaM+c8V7oQrt8bk7zzzSx8bOD9XG//o9975mrIhaeJAg5sAMjqGKW4exMz+hinufMt+0ycFz5qUxPlwveI6DXzc1H6H6Ye8ZASNgBIyAETACRsAIGAEj8HoQaLHCw+uB/48bJd4OhCmwuo0XR2OFle0RI0Y48ft4WQQb/43Vs/dfPYFXMRe0+SraffU07ApGwAgYASNgBIyAETACRsAI/FkImPDwZ5mpF+wneQ3InUAOBVziWeX2eiRgfPIeoQzE+uN6T6iKGaUvCN6qGwEjYASMgBEwAkbACBgBI2AEXnMCJjy8JjcArvQkt8Trwc1hQR4GPBzc3QrINUFSR3I9kB+B8AQrRsAIGAEjYASMgBEwAkbACBgBI2AEXoSACQ8vQu9PVpcYfBJzsn0mOz6wCwU7ILDTBDt2sNsEn7HVpXk6/Mkm17prBIyAETACRsAIGAEjYASMgBFooQRMeGihE/MquoWYQJJAkjeSrJAdDNg2k90peE6ohZvY8VVc39o0AkbACBgBI2AEjIARMAJGwAgYgdePgAkPr9+cOyNGhGAHA3aUcHcjMC+H1/RmsGEbASNgBIyAETACRsAIGAEjYAReIQETHl4hXGvaCBgBI2AEjIARMAJGwAgYASNgBIzA607AhIfX/Q6w8RsBI2AEjIARMAJGwAgYASNgBIyAEXiFBEx4eIVwrWkjYASMgBEwAkbACBgBI2AEjIARMAKvOwETHl73O8DGbwSMgBEwAkbACBgBI2AEjIARMAJG4BUSMOHhFcK1po2AETACRsAIGAEjYASMgBEwAkbACLzuBEx4eN3vABu/ETACRsAIGAEjYASMgBEwAkbACBiBV0jAhIdXCNeaNgJGwAgYASNgBIyAETACRsAIGAEj8LoTMOHhD74DAoGA6uvrVVNTo+rqalVVVamuru4P7oVdzggYASNgBIyAETACRsAIGAEjYASMwB9DwISHP4Zzw1XKysp08+ZNxcbG6tChQ9q9e7cSEhIaPrcnRsAIGAEjYASMgBEwAkbACBgBI2AEWhMBEx7+4NlMS0vThg0b1LdvX7Vv314//vijVq1a9Qf34tVfDs+OjIwMHTt2THv37hWvmyp4fty4cUNr167V7NmzNWvWLK1evVpnzpxxPEP8fn/I6tRLTk7Wxo0bNXPmTKcebZw7d04VFRUh6/AmHieJiYmKiIhoqLdmzRpdunRJpaWlIesxBp/P5whFzCF9pK8rVqxw3musXsjG7E0jYASMgBEwAkbACBgBI2AEjMBrQsCEhz94ogsKChQXF6f169friy++0D//+U/HgP2Du/HSL4dBnpeXp8uXL+vUqVPatm2bM64hQ4Y4hn1jwgEdgQkCBcLB3LlzHTYICWFhYZo2bZq2bNnitB3cRklJiQ4cOODUmTNnjiMiUG/BggX67bffFBkZqfLycnnr8RyxAk+TefPmOQdzsXnzZs2fP9+pt3PnThUWFj7BCNGhqKhIO3bscM7hGtRDuFi4cKGmTp2q/fv3O/18oqK9MAJGwAgYASNgBIyAETACRsAIvOYETHj4H90AGOq9e/fWu+++2yqEBzwIrly5oqVLlzrG/ODBg/XJJ5+obdu2Gjdu3BPGvxc5QgBhJ6NGjVLHjh114sQJISjgrXD27FmnLgLNkSNHVFxc7K3qeENwnX79+jmfkzOjsrLSET7Gjh2rn3/+2XmO+OAWRAfEkW7dumn48OE6fPiwI07U1tbq9OnTGjp0qPr37++05xUsaAPB6Ntvv3XqIZTg4cD7Fy5cUK9evTRs2DCnPXJ4WDECRsAIGAEjYASMgBEwAkbACBiBhwSeS3hg1fdZLvMGtnkE4IiB+69//avVCA/kqiDsIDo6Wrt27VLPnj2fKTyQ8wJPh++//97xcPAa+4gEiA9vvvmmfv3114ZcGLDDuB8zZox++OEHLVu27In7ks8I7/jss8/Up08f3blzp+HznJwcjR49Wv/5z3+0detWITi4hXbxmCAEBjHD6y1BGAj9/Nvf/ubk5ggOq1i3bp0TNkOf8OB44nsS8MtfX62K0mKVllWpus6n0IEjbk/s0QgYASNgBIyAETACRsAIGAEj0HoINFt4YIUeQ5BVZa9x2HpQ/LEjwTAdOHCg2rRp0yqEB8bD7hx4KnCfXL9+XYRZPMvjAe8DBBi8Hfbs2fOEwU6bt27d0qeffuochEdw7yEWkCvjp59+UufOnUVoRHAhNwSfvfXWW44nA32ibmpqqiP2fPfdd45XwxMCgaSjR486+Te+/PJLnT9/viFPxMmTJ0Wdd955x/HsCP4O4KmB0NKlS5envCUCNQ9UcOOg1o4fpolzt2v/lUwVmfIQPGX22ggYASNgBIyAETACRsAIGIFWSqBJ4cE18Ih/ZyUXQ/mXX35x4tkxEvPz851ke38km2BD8Y+49qu4Jm3C8v33328VwkPwPOBlQOjBs4QHciYQEtGjRw9HIAhmjcDA54SkkPzRDW+IiYkR4gCeCYQ9BBc/u0CtAAAgAElEQVQEjUGDBukvf/mL49lw//59x4Ph+PHjeuONN5xQC0IrggtJKUeMGKGPPvrIyd+A9wKCCuLGe++9p//+97+OeBFcjzATrvfVV18pPDz8ie+FvyBVd/dNVaeP39fHnw/XrB0XlVoT3IK9NgJGwAgYASNgBIyAETACRsAItE4CjQoPxOzjOk+yP1aWSYKIEYerOav0Xbt2dXYdwMDEG+JVFtzjWcGOiopyEg3iDr9v3z6lpKQ4HhhcG7f4+Ph4J9kgK+Pbt293djvARR5jkvdwrScEAKMU0STYyKUdVrLdthBXSGxIPRIVXrx40XGjD17tduuRm4C2SazIrgdu0kL6yg4PMHUL1yY/AcIDLvx4CiQlJTnj4loYuvTb6+7v1v0zPDZXeCCB5Oeff+6ERLCjRPCcwA1PAjwNuBfT09OdOVi5cqU+/vhjh2EoAYEdK0aOHOncsyR/vH37triPuHf++te/OoIFfIML9xD5IbjHp0+fruzsbOd67DyC9wReD2yHGlzw8CA/xIcffqiJEyc6oSDuOYGyDOVe2KRZg/tqyJhwbTl9U5l17qf2aASMgBEwAkbACBgBI2AEjIARaN0EGhUe7t27p0WLFjkGGILD//3f/z1x8B6rvxjlGPGvomCEsuLNNdg1YPz48Y4xSCJCjHZ2IXCN1QcPHjgiAcYfMfokJGS7Q1bJMVIxWjEIqTdlyhRndwKMWG8hN0Bubq5j/Ls7KnAuOytwbbZORESgntdARnjJyspydjWYNGmSs2JOH0leSNJBtsxEhPBezys8sMKOWz9iCUbyhAkTnH5iOJNUMTifgLfPwc9pF08VBIsXPWgnlMgSfM1Qr5srPOBJg/jC9qIIXV6utJuZmel8hscDXK5everMEfNBPeabpI/B5dq1a04uB+5TkltSD8GAOUR4wHuHpJDBBc8F2kbo4BzuP8I92CUD4QERjtfBhfeY77ffftsJHXkiwWSgRjVl2boVF6OzifeUXlglSz8ZTNBeGwEjYASMgBEwAkbACBgBI9BaCTQqPGAEd+jQwVkxDhYdvK8x/PBGeNkFg5e4fAxxVpkxBjECKWx1iJcArvYY7XgLsOMBBijvIzrQxw8++MAxPonbx2We7RBXr17ttPf1119ryZIljseEa+wiXuBpQD3awQMBUQFXe1a+MWDZDQEhg6SIbuHaiCO02a5du4adGVhhZ7tF+kKf6IdbXOGBlXXGMXnyZBE+QFtsS0k/Maxx3cdro7kCgNtXBJnmHHhoNHZg9DMHv6c0V3gYMGCA/vGPfzgeCHgpuHPhXpM+kAMC4cEVGbzvEc5BAsrggvCA5wLCgysyMJ/ue4RFhBIe6AMCB30ixANPieD3QgkPjJd7EY8g6jEPVoyAETACRsAIGAEjYASMgBEwAkZACik8YGzjZo7x5RUZQj1n5wAy+r/swmr9qVOn9M033zjGPqES7ioyRjifYYi6hj5CACv0GNusorsr3bx2E2JSD+8BPCVw02cnBdz0ES74DO8CvBPw5MBwdY1HjGGYsGpOXgaSFtIftxw4cMAJByBkgJwFiBy0h/fEpk2bHIb0E2HBLbRJW6ysk5Bw//79TigG9TjIRYDnBvkISFwIj+YUrk1/8LhozsEODxwwwyj3HogweHL8ntJc4QFPBzwJyNUQSnjg+ggHCA94qzBfhF+wFSns8DIIFTKByIBQxH1A25zD/LE7Bu/BntCZ4MI5eMbQJ+4PRAY8MWiL7wNiXCjh4e7du47wQNvcQ+69E9y+vTYCRsAIGAEjYASMgBEwAkbACLxuBEIKDxjqrtEWSmzwvkfCvcWLF790bhichC3gEUBfMP68BcOWUAqSAC5durQh3IO8D4RlYAAuWLDAWbH21sPgP3TokBMCgWcDAguJB/FoYBz//ve/HaOUVfXggkDBajj1MNQRQvAImDdvnuOdgMFJPQQQCjkd8NJge0l2ReA6bqEfrLrjms+qPaEt3oIXCXk0YH3w4EGRP6I5hWsjeGB4P8+RnJzs5MTwPsLkZXg8IGYgpoQqzxIevN4NoYSHxjwevN4NeEwECw/P8nhAeGCrTkQG8j48S3hwPR5MeAg1y/aeETACRsAIGAEjYASMgBEwAq8zgZDCA8YrRj+r7V6RIdRzDPXly5e/dIYYfHg7/P3vf3e8CRAXSNroHuRuYLtGwhSIvyc8gcJ2ieRkwAAklCLYoOccjFLGh3FJGAfnYJjiKs8uDOR1wHgPLogFM2bMaFj5xjB3DU7aIp8DK92c5xY8JfCycL0u3Pc5x/V4IIwk2KOBsJHu3bs7/PGGIATjz1S8Hg+INI0JD3gzkLgUrwTyK3jZMV6EB8QJPB5ckcErRrjvBbPx5nhwRQavGOGGXwTXc3M8BIda4AXhvhfK44H38L6wUItgovbaCBgBI2AEjIARMAJGwAgYgdedQEjhAeMPQ59whFBig/sexj2u57jkv+yC58K//vUvx5AjoR8rziQGdA/EBkQAvA24vusRgPCAF0NTwgOu+oRbkGQQo5ccChj35G9w8zt4vRO8Y8PLApGBfA54YSAQYDTjucCKfHMLjDmfcAHyOwS75pO3gC0mYU1Cyz+r8ECeiqaEB8JB8GpBXMCzIFh4YK4QdOCE5wTMyZ1BeAj1Gssx4g2r4FwEBeaZ3B3MO7klSOgZXNxdLZhP+oQoRVJKd97xagklPNA2fUGcIAzEDQty2vfXqa6yULl3b+pO+gMVlNVYcslg8PbaCBgBI2AEjIARMAJGwAgYgVZLIKTwwGhjY2Mdw7gprwe8ETDIMPZfdqFNjFZWkBEX3MSSz7pOc4UH2kScwFD0Cg94PLjhF6GuxXgZN8IDRqpXeMCDIdhwDtUG73Eeq+4YuAgPeEZ4C8ID20i6wgOJMZtT8CyorKx0EnCShPNFDkJLnjCgm9OBR+e4Hg/MIYZ/Yx4PCEDkCcFYJ+dCMD92AiHkBE5z5sxx7gN2USEshjAbmJPvI7ggNOANwRy7ITd4SpC0E+EBUSFUbgj64IohhOzg1YI3TXh4eIOHTKjtNAlRQUjCA4hwHC+3QPUDFaQe0oYZYzR1YbT2x2eowLa1CJ4ye20EjIARMAJGwAgYASNgBIxAKyXQqPCA6z+eBIQ7YPxiwHk9HXjdp08fZ0cLN6fBy2SE4UoSRzwS8GxAHGhOaY7wwGo44Q0ICOwaQWJADH0MVQxl3OpDhVpgPGOMIoaQeBCjlLquiz1JIoNDLRrrMwY2q+6M72UKD+zMERkZ6bCD34scJA1truATPE6v8IAh35jwsHnzZieXAmEliF3BwgMeB4TD4GWCaEASUbxbSO5JElDuQZKCBhcEBMQF7lOugQcL4g2eLcwfcxVKsCC3BiE8H374obMFKiIHuTqioqL05ptv6tNPPw0ptHE9ckkQ+rNs2bInhKT67HilbBikT959W++8940GLj6gY5lPCk3B/bfXRsAIGAEjYASMgBEwAkbACBiB1kKgUeEBQxHjG6OOfAjffvutY5RjkHXs2FFz5851XNUJAQg2Fl8GHAxoDE2uR8gBCSGDC9dmZRsjNFSoBaviCAPBZc+ePY7hyeo0oRusaGOYEl5CLgGM9VAGNzxGjhzp5IHA7R+DlOSLYWFhzuo7Ig25BUIlZETEIUyAXA+UVyU80D59Z/eHFz0QD/B6+D2lucID4Q6IB+zgwY4g3nuJ5whOeKFg8DNveIbAHa8DxB9Cfdj6NbggKhCig7ADB+YEgYz7hfbwWAl1T5HIExGEHUoQExg//WBHEkQq2mMegz1U+J4QqsO9w7apXqHFX5qu7LPrNK3/T/rmw4/Vc0aUtl83l4fgObPXRsAIGAEjYASMgBEwAkbACLROAo0KDwwX44kVZjwEMO4jIiKc1WOMMww/jFyvofgyEdH29evXnfwJGHyEOBDv7xZc2TEAcYEnBwIJHClejwdc3/Fk8Hpk0AahFLj348LPdpuEJuCpwM4T5BPAMGWMbngDY+R6u3btcgSLYCEEI5dVclz/8c64fft2wzUxkhE/uCbt8xqjlfFhcBPq4SaXhDcH12ILTYxYvEzYFQNhxGvMuhxCPbpt0M6LHLTzPPPLuYyNAwGGpI7sekLuAzdsI7hNBCbmltwJ5O2Aj3tNxCQMejwN2AaT+abQBu2R94N6ixYtekIgwVuHJKTMMfk3mA/3uuyWggeGuw2sK1jRLnOC2IVgQQgHW5PCj8K9SNJSvCUQOvCEoLhjxssB8YS5JKTDHYNzUn2lqvJTlHhogUZ36KTRi3brYJp5PDhs7B8jYASMgBEwAkbACBgBI2AEWj2BJoWH//XoMWAxujEecZvHKLx8+bJj1F64cEGbNm1yjE5CHtzkjF7hge0QV6xYIc7FcMQYpg2EA1an165d+4R4gkcDq+4YnhiZCBqskCcmJjpu+Xg5kJeBEARyJ7gFgQJhhnbxeli1apUTgsIuCm4/V65cKRJm4qVBe4cPH3aMZkIIyOVw4MABZ2yEFrDSjhcFgguGLrtsIHqEyi3g9qElPJL08fjx445oAueff/7ZSQqJhwwhDngNkBfDm7gTAx1xgXAV8jwQ6oDHBsY7oRfMQ7t27Zz6CAHeAkPEJepyXUQFDrdep06dnkrMiWDB5whMiARcm2vRd/J1MMfcb4gLiBVuQdhCYELoQPCgHiIWB/ckQgX3Bp4t3nrUD9RXq7rwtm6fWaZpw2cqfMcFJZc+3vnEvYY9GgEjYASMgBEwAkbACBgBI2AEWiOBFi08ANxd/ccgJNwDN3jCHTDyCPdATPAWjHvyMBDbj0HKuaxwY8CyXSZGLCvweDSEConAwwMDmboICazak/sBt35W3XGjdz0hvNdl5RzDlXOJ86cu1yWxIjkjWHVHHMF7hJwOX3zxhXMej+QqoF94TSCm0M/gz3lNKEhLLjAlJMLtO8IJBzw4CF9gPuDrLYgBGPLkSECEQbhB3GE3DEIptmzZ4ngfeOvwHE8WBB/EB+qRywGvHOYXbxKec84T3geS45GBQECODY7169c7eTG4p3jN9fCCCS6ID4hReMpwfyCu0E/qIZogDoXafaS+LEuZ57ZowYCOGrrogI6k5Ks2uHF7bQSMgBEwAkbACBgBI2AEjIARaKUEWrzwAHfc7/EwwHhnRZy8AIQvYOy7ng7u/Hg9HhYuXOgIE6xK46ZPXTwfcO9vLEyE1WquhwGJhwGr2RxsociKO/WCV7S5Nu9h5FKPPtBHPBfoMwYrAgoGMP2l37jqBx8IGhjhtBH8Ga8JIWjJBTawDdV39z3GxnneAhdEIHawwDNiw4YNzoEXCPOFCBCKuVsPxogeeLAgIpC7wa0XLDq41+V6zC/nUoe6XI/3uF6oevSBvnMO13P7iRjB/cjcPd3PgKpzb+jGkY1aHHlGCRlFKq3zy/wd3JmwRyNgBIyAETACRsAIGAEjYARaO4E/hfDAJGAIYrRjxOOVEGolm/PY1pDQBDwelixZIkIXqIfRTl1EhaeNw9DTzLnU42jseqFqeuvx3ErzCDBPiDuEPnDwvDn8mBuEKQSm56lH29SjDnWbez3qBfeTvocSKxi5r7pc5Q+ydO9+qSpqfXocwNE8LnaWETACRsAIGAEjYASMgBEwAkbgz0zgTyM8NBcywgOhDAgP7GqB8GDFCBgBI2AEjIARMAJGwAgYASNgBIyAEfjfEGg1wgMu8LjqE2dPzD3CA3khcKVnRZsVaStGwAgYASNgBIyAETACRsAIGAEjYASMwB9LoNUID+xKQJw+uxWQrLFNmzZOYkN2wyDpoHfbxD8WsV3NCBgBI2AEjIARMAJGwAgYASNgBIzA60ug1QgP5G9gq0Y8HEj8R5JCkv5x8H6oHSxe32m3kRsBI2AEjIARMAJGwAgYASNgBIyAEfhjCLQa4YFdI0g6SbJAdofg4Dm7KfC+z+f7Y4jaVYyAETACRsAIGAEjYASMgBEwAkbACBiBBgKtRnhoGJE9MQJGwAgYASNgBIyAETACRsAIGAEjYARaDAETHlrMVFhHjIARMAJGwAgYASNgBIyAETACRsAItD4CJjy0vjm1ERkBI2AEjIARMAJGwAgYASNgBIyAEWgxBEx4aDFTYR0xAkbACBgBI2AEjIARMAJGwAgYASPQ+giY8ND65tRGZASMgBEwAkbACBgBI2AEjIARMAJGoMUQMOGhxUyFdcQIGAEjYASMgBEwAkbACBgBI2AEjEDrI2DCQ+ubUxuRETACRsAIGAEjYASMgBEwAkbACBiBFkPAhIcWMxXWESNgBIyAETACRsAIGAEjYASMgBEwAq2PgAkPrW9ObURGwAgYASNgBIyAETACRsAIGAEjYARaDAETHv7wqQhICijAQ8jCZ019HrLSa/DmQ26vwUBtiEbgJRLwKxDwq973jCYDfgX8PtX7n3HeH/qx/Q7+objtYq+WQCAgX3WZSkvLVV5Vq2d9JV9tZ6x1I2AEjEDLIBCoq1JVeZmKSytV61hILaNf1otXQ+DPITwEalSUflUXDu7QxmO39KC8Ro//Pg5IgTrlX4vRySMndDLhnvLqfx8sf0mm7icc1pq1h3Q5t1QlQX8ZBCpuKfHEQR0+cl7x2eWqyr2mq6mZyi2qat4fEfWFyrkep2M7tmvbhXwVVNQrWH/gGleOH9CBA3G6mFXz1OehR1Yv+fOUcnKfjsUm6Ep6qYfPoxqBClUUXFfcts06fDFN6YXNadsvX32damuqVV39Mo5a1fn8T/fN6WJAdRVFyj63W8fiknU9p1TVDhy/FKhQdvxBHdq1R/tOJSutItBMLo/G7i/Tg4x0paflqMh7b/hqVVtVpuKiIhU1+yhWeXV9IwZaQLVFGcq8dEgRe+J1p6Di0Ri8s+ZXeVqC4mOPaEfsLZX4As27dxRQXWmhyioqVVHvvWt8yr95XmeP7teuM3dV7gs0wtfbB3v+WhDwlyon5bRiDx7QzrhMldc18d17kKrrZ/ZqU+RhnUuvUmmN9x7z0vKppvSB7l9PVFJOhcprH/8Se89qeO6vVaA8RecP7NDB41d0Ncf7BWw466kngdoilaXHaffGSB2Nz1RG8I/xoxqB6gKV3onVzi27dCr5vnIrGuv3U5ewN4IJBKr04NZFnTt2SPsu56qyzt/I76xfxfcSdOnYXm2Lua4H/B4GtxXytU8lt87p4pk4xV7LVin/dYc87/nfDNSWqzo3SedPndDxmGM6dux5jhgdP3laF24Xq7jS99L69MQoArWqq8hQ/M4VitgRqzPX81XzxAn24mkC9cpNOKKje/Zo96mbKvKrBfzf5lNNSbbSzuxU9JYo7T9/S7cLal/NPfM0kD/gnYACvloVXjum40dP60xylsP9yQsHVFOSq/Qz27Xz0GWlZJeo6mV8kf0++TIv6PShQzocl6Lb5S+j0Sd7roBf/oKbSrl8WZeT05T9OzoeqC9XZXmZikrrnph3f0mGcuKPKmLTUSXcL1dp834Ugzr4er70l9xQcuwubVq3Q0fuVaus9hXM/euJtkWO+k8iPJTo1sn1WjTgR33w635dyy5t+EMn4KtWfcl1HZ3XX4N6D9OIBdE6cTNNaWneI10Z2XkqqAp4DMaA/L5aVZUWqriwUIWFhXpw9bBOLR6kt9/ppwUnkpSY+/D9wqISFZXXqu7+fq2fMEhDhy/UyvPZKry4SovDtmhv3G3lVj35B7ivplKVpUVOu7TtHLlXdTZ6riZ1/Ulfjjuo2JQM5RY8+qywQIWFBcq6uEbzhvdXv6HztCouU/mFhSpw6xeXqqSi9un/fP0VClSd04aR3TRiylptiMt72pitzVL2lQiN+/w/GhZ+RmfuVjzxoxny7gyUKvdmvM4fO6D9+/e/+HHwrBLTHoT4jwyJs1ql2Qk6OPZ7DZy6WTsuZaks4JevtkQld08ocnJXdfm+k7r8ukRb4q4pKTlZyc6Roht3s5Rd/OR/At7x+MtSFbdzk9av3qYDV3NUXBeQjz96S9KUlhCjnVGRioxszhGlyKidir1eoPuIH9UlKs+6prOnYnUqNlaxsSd0OHK5wkd3Utvvx2nhlj06cIL3Y3X63CVdyaxSRU2Zbh8I08JfB+jniTt0NStXWffzlJf36HiQr7zCCtXUe/7oD/gkX5Fux+5VzInzOn+3yCNo1Cplz3zNHtZHHWccUU6tX3Xewdvz15eA74FS94dpwfD+6jA2WhfvVweJVo/QBKp1/1yENozrrs/bj9PqhBLlVj75e9YAMVChgptxOrZ8umasP6KTyTnKr6h7+jfJreArlz93mxb0a6+BY9dqw6Vq95MmHn2qzktUUtSv+vGDLzRgzjZFxSbp+vXrj487OcorLldpVryubPhF3378o4aH7dSO01eVlJTkOZKVejdHOcXNEVqb6FIr/chfX6uasiIVFFequvaBEveGae6IAeqxNF4PGp3XOt2NWa1lv3ZXu1E7lFxc7fk9agRUwKdAVabOr52gaRN/07TIS8qpzlda8lUlXUlQQkLzj2vX7yq92C+f5xYNlGaoIC5cE4YM1eiJ0zVj3kItXNiMY8FszZo6Tr8OHq4p227oxv3qZ/+/2MgQm3rbX5Wn/KRIzez8lfpO26yoy/dNeGgKmDMLNYpfO0KjevZUt+n7dLdeqvuf2iMB+SpzlX4hWquG/KCfvmmnzmNXacPJu8pvVKhtcpCv5sNArXy1ubodf1EJyenKKKh6dE8HJF+Bsm5cVWLiDV3PLg/xu+2Xr6ZQCSsGaNiwGZq97ZKyghbgFKhR4Z047R37tX4aukY7L2c6IuILD8ZXo9q4BZo2YIAGzdyqgzmeL/gLN/6oAX+d6pK2as1vs/Tb6oM6nfe81/CpIuOSLsce1Z4Tqcrx2BT1907r4ooR+vDjYVp5JUfpLemeeFn8XlE7/uIrOh0xTcM69lDfNdeUWlAt0x5eEewW0GwLFB4QBOpUV1Otyhqf/MQkBEp0I2a15vT8Um8N3qPErEfCQ8CnupJ05Z6eo+HffqgPP/5RP/Ubp9kLFmhBwzFf8+cv0tI123X4rs+jpNWppixHt0/t05EDe7UHVX39fC0d0V5vvPmDhi5er3U792jPnr3ad+i4Difkqihzr9aN66/Bv8zV8tMZKj43X6M6dNbwuTu073qZHi9E+1WelaLkuEPat3ev9jYcuxW5fLqm9/pCb7TprQnhEdq02/18j/bu3aU14zqoww+d1XHIb1rb8Nmjc47G6dS13KdckQJ1hfLlbNLUn9tp4OQtirpa+dQfT76iJN04MFXfv/mhRm66pks5wf+bhLgb61N1OGyIen/4lt58+196//33PUcbtWnznt59+229+14b/auN97Og523+pTZvv6k3/t5VYzedVkIo26M+T7nJUZr6+Vv6dkSEtl/MUlVduYrSL+jk3A76+bO2avNRO33TqZ+GjRihESNGaPiQQRo0cLDGzNmgLZdKHDEhxChUl35EkTP7qct3ndRh1HrF3CtRQbVPNbf2ac/sbmr7xlt65/22er/tB/rggw/Utm1btX3/fbV5v63znPc+aNtGbd57R2/+9X0NWHtF57N88t9P1K3o0frm4w/10b8f1W3znt7755v6yxv/1Ltt3ldb6n7woT77rrsGbc3SnexkxSwfqSHtv9c3Q8K0actWbY2M1NatW7V16xZt3bZL244mK6Ok6rGA4K+Wqq4oanxn9eo2UqNXntSN0rpH461V0vZZmjagi76deOClCA+E+tTX16umpsbxdKmrq5Pf/+z/oKnHua53DM95789S3P67466trW3WuGHjHTfsWsa4fSq6ukvbpvbUl5/31tRj2bpbUv/UH5uBmnuKWz9RI374Rv/tu1In8mtU2tjPQ/0DZVyM0ooB7fTxB1+r16xt2nslRyW1HqHMO+H1ZfLnbtXs7u3Ue+RKrblY5f005POAr1RZl3dobb9/6713/qNvewxQ/2EjNGL4CI0YNUZjxozV+HlR2nfuihLPRWtlz/f17rufql3nHurRr7/6cwwarCFDhmjIkGEaFxat7RdDiLFBV/fj7okRXlDseHIgTjZWHhrshSrIL1Z5TZ3nt99bg9XDOtVVVaispFhFhQUqyM9XPgeCc3GJyiqqVVPne2pOvK08fv7w/8aaqvIQ7RWooLBIxSVlKqtsbpsB1ZbkKuvyYe05maJ7+Wk6v/03TevXVT/Nu6D7rvAQqFd9TYXKGrzC7it+x3zN6v+jPuq/TnEIO//P3n94VbFs6//wH/W9+2xzxoSgJJGccwYl5yAIIqKCEcWsiEgwoQhmkhgIEpWcc45rfX6jGpYskuI5e997zvueGqPoXl2dqqnumvXMZz7VO8sa6x8cZmRycYiMHNnkAEMVNzjuoI+5cwwnn5bS15XHVW9H3K0ssLBYlM1MMDEywtDIGBNzC8x/lFvhFnyapOIJ6TqKZyPrqaItyxed7do4RV7i8sMX5Obm/jrnpHIz0R971d0YxRfzoXFklf8LxZVXs5xmpKmY4gQL1FWdiMn6QsWQcuOaYWJkgIG+fvqHxiWninLpwitMSyy9wb4+egbHmZatwP6TzzA9Ocbo8AD9faKdKtpdD719/QwMjTA6Mc2MbOUrKV9XLptmamKM0aHZ8/X2zJ2vW7S7Xvr6BxgcHmVsQrABV3dO5fMvXRfn+KuAB2FDzjA1NshgXw+9/UMMjf2+O1o+PUZPxSOyTtihtl0HO0cjNLQtcDp6i8d1wwjy119R86XP4je3zPQw3vWAaMMDmLhf4MqrpllHnXwaxl5xM8gGa8sgAu9WLrElEcyc4Toy/TWxcI3hZFYlS4gHMz00f7pPnP5mNP3SeFbeNW+r/OatLth9eoKJV1H4W1hiHXKdh60rdUILjvq9H4Ll+i6WECsLrAOTyWj63WuM0/DsNCfdzTlkH8ONsn66x8Q4Baa/v6Iw0YUNm5xI/NhKw18APAg7QmGLCbvk38e2WN1jV75/YReufI4YEi4AACAASURBVP8TtJekcdvfkI1aUdwr76Dzv8jD6h7yf+Be/4bAwwRDbRWUvXlMyts2ekfE4GV54GFmqJH6t5cJ09/KHpU97FHT4uAhfQyNjDCay4YHD7B/7z72Gx4hpnCSztG5rkE+RM+3l1y3U0F7jxhEb2Prlk1sWr+G//c/f7Ju0xY2b53drnLACM3IN1RWPeBqkCPurrGcLx5FNtFO8UV3jjj7E3DpJdU/bOopGnPOk+imgcqCwfp2afAqrvHHP9azccs2ti0q37pxHevWrmPdhs1sXVSmcsgVu1Ov6YMFjAbZUCvDL/yx07El4HIu+b2LB4kyRr+95t0ZezZtciDhXT3fVkMxk4CHw3iYWmIZlUbe6ze8fvOGNyK/zuH5kxQuxcRw8c5Dsp6/mt2uKFdavnr6gJxYE7T2OhC4AvAw01NGVUYw2ut34ppUzPv6bjorn5MZ64D2NhX0nQMJiDnD2XPnuXDxAhcvniXGwxBDDW2MPBO4UT4qffyXewcFK2bg+xueXvDDVlsTdcfzPPjSQWv5Q7JiPdA84MaJvGJeffjIx48f+VD4nvcvX5DzupCiDyXSto+Fj8m+E4LJuh14XZ0FHpgaZaK3kZrKCr5K7ItyPjy9we1gEzZoR5D0+A2F5YKZ8ZXK6m809EwyUHqTc95GqG3Zxo59B9DU1JTy/n172L1rJyr7jdHxTOHt9x7mWePCcJqi9+N1Er0tMDLzwu9WGd1TcqYZpzztGBFO5hwMffqXAA/Dw8PU1NRIdOVnz55J3kjB2PnVYFocV1ZWxvPnz6VjKysrGR0dXe5f8m+5TXTsX79+5eXLl+Tk5Ej/99XUe3BwUPKwZ2dnS8cJz/zIyMi/RR1l441UPD9HqJ4qe51ukl3Vucg7JWOk7BYXfU3RNXTH614dfRMrhWSIKsmYGe+huyaPB6HGGGvqYhGUzO0PXZIRu6TSvw08yBlvekPexSMY7NiNtq0XHt7O2BhqcGDvAbRsfAkMCiHsVBrpD65xP9Ed3R170HMNwT/kCE6mWmioaqLtGErksRiOH48l4fZzcsp7F3wzl9wnMoZKs8iKcUTHwJOEon6al1jciqNkDHwr4FWsOdoa7pzKraRK0a8odhFL+TjD7V/5lHWBuABn7E0Oon1AjX1qB9DUNcHCNZCoy094WdFO/2rGQdND9Dd94t39RE4EuOJopoeuhjpq6vvZr62HgZkdLn7RnLz2lLzSFgZ+ORCakeqRd8ICLfcbPCsr4+39pcCDfOwbNW+SibK1xNbKEktLCwy196G6fRNrt2uib2aBhaXYbo17yGkuFYwwqmw0zgwx2vqeFJ+D6B9ywf/SCz719DLaV0xmfCTHQ4IIClqYA11NMNRQY+c+fSy8g/D/UR5CzLnbPCifZEzJ/T0LPPigs20PulauuAWGERa2muzDESdjdFR2oX/ybwIeZjppKLrJcQMV9rrd4klZByM/umgRP9DBu0t++Nm74x73lGrh2VduR8rrMw0Up8URbG2DfvAjqvrGWG58Ixtpoa4wk9vxARy2NUJfUw31fers19LD0NoDr6gk7rz9TlP/xC/ei9mLT/V9o/J1KtdPBOBpb4Khzn72q6mxX0MHXSNLrJz9CDl1ndQ3VXzrUnjYlW/8d9f/QuBBPsX0WAvl9yMIstbFyDma41n1v3lDMka/55AZ74aVvhnG0Tl8qs3n/nFXXExtsD16n/yuGcZ/dxz7m3exqt1nuhnrSCFcaycH7c9w4UXDHPAwBWM5XPU2wdjAC88b5UuBh5k+xjsfE2eshkPoHe6U9C9pH/LhKr4+i8Vu+y4cLn2iqOmvYQnJpycYyYnAx8wam/DbPP1tNsIqns6/DDzImR74SsGdY/gYHGSf3XkeVfXQMyln+ttL8hOc2bDJhXN/EfAwNjZGdXW1ZE8Ju0TYZcJO+U9J4l6/ffsmAcDCPhJ1EXbi0iRnuquEL6kBaK9TxeP6Fz60/v9SCNPSGv//85Z/Q+Bhkt7KF2Sf9kTfPpGsii66xvsXMh6au+huKOL17RiC7Y1R13QgPPkRj14X8eHDh/n8PoMrobZYaOigYX+aJx0ypbiraSaGO6kvyuVNXo40YHh2J5EkXyPW/GmA18W73H46u/3Fq/e8ruhksCubmyEOuDvHkFgkXv5phuoeci3cA0/fOM6/75nzlsgZ7fzOty/5cxT8Wbq9oNz/Vn6Vx+uch2Q+SCfryXNy3n/hY123ZJSIbnmys4bqgmzuXTvPJS8N1PYZYxdwgrN3Urn/IIu0J5/4Lii0smGaCu6S5LSPP3fY4H8mmaupaTx48GBhznrJu9JGuhVG0Rzw4Gnrjsf1cnoHBxkcGmJoaJDe+mJKUqOw1rYh8nYuz0sq52nQEiX6G41tXXQPDDHQUkXbXSeMDjgTtBzwIJ+gq/QxaSFGbFb15sKjbNJTznHSywYzQ3PMw26R/q6U8rrv1Nd/p7aiiMKUMDwNtTF1iSIuQ9RzihmhHyGM7WXcDrKJAbprXvMiORQrTTMiUj9S/CaNrFhPDuoGc71hkJahUcbGBmkoySbthCcOZ15T1tDN8NgYo/2V1Lw7he0GFbwVwIN8hon+FiozTpJwPJzQ0BACDtvjoLeHP7cbYOPhg39IKKFHTxF/+QnlQ43knHXD1doZ++AkHr8vIL+ggIL851yPcsHV0hzjo+m8/tJM98jkrLGg9HWaGW7ha/ZZTnnbYmAdzqWSATqG+/mUcpRQJ0v0onLpnZItMRSUTvHTVQEsvHr1ijNnzhAdHc3Fixc5e/as5Dn28/MjNTVV8tYuBiCGhoZ48uSJdEx4eDghISG4urpiaWkpDSgEECEG8P+uSXTuxcXFHD9+XMoXLlzg5MmT+Pv7c/jwYW7evEl7e/sS9oMAVUTdYmJiEPWOjIzEyckJOzs7goODycjIkACI1bBF/ppnM8PkSB9d9TXU1FRLnXx1VSnFz29zI9QBE+8k7uQW86lqrqy6kurqD2TFOeFoYICeUzRJeV8or5wrr2mgsb1P8ngteKXkM8xM9Env04Pjrni4BhOY9Ibvk8vEYP8m8CAbrOD97aP421hwyOUcGcXlfKksIfdaBOEOZmg4neNhSTmlNZ/IvRHFUSdzdFzOkVVSSXnNF17djOKogyn7bc+Q+alS+mY0tHbTPbRyKNbss5cxUJLCvUBDtu22JuptLw0LvNLK/6EZ+qtf8yxYgy0bzTn6uIzyeZRwdsfJTr69v8e1cCcs9LTQ0DHG3M4VN08ffH298T7shIPpIfQOGWHudoz4lHyqB2QrMCdgsqucooyzHPe0wuSQNjoGFlg7uXPYywcfH2+8PJxxtjXB4KA2hxwiOXbvMwJ/VnzKle9+fn2G7sqXZAQeYJfrbV6UV1CwHONhooXmL4+5Hh1FTFQUUVFH8XM0xmj/Tjbut8E7NIIIaXs0py6nklUxxvgc/U8+1UfH1+ekRTlgtF8Xl9gHPCrrZHh6mpmpATrr6/hWI9rrwlyRFUukowU6ZsEkvK+irGq+/FtjKx1DsgUMt3nGgxa2IQkk3hMMQgWb8CfLrOskxXlhs2cXhn8T42GqvYiC6/4Y7NTE+YYI4RxX+r+IMLo2cuMdcdQTfV065VM/CSmYqePdzVDctXXY5X6f8p7RBWEuIj5/ouU9jy+E4mtjwCEdXXTN7HHy8Mbb1xcfT3dc7Sww1tVB18QJ38RHPC/vZHjZhiJDNjVAW/E9rh07grOZPod0DTC2dsb1iA8+vj54e3rg5mCNlaEOmpoGHD7/grzaVYRxImOko06yX5YP43zE48eZnPUxwVTnIJp2kVx9+JisRyuEfOZ9oqyhZ4nWgNCK6a5+SdapIzgZqrN30zp2GvkTdK92/jX4xZp8ZoLhumxSjrviYGGPbdA1ntb0MzgxRMeXDG5FuWFpYIXt0Xvkfhugbzkk6BfX+EuLJeDhLmGaKujYneZ8zkLgIdnLGCN9T45cVwIeJttprnhPTsoVbiUexmjXHgydQgk7f4fU+w94kPGcoqZh+idkjH1/ycsEe3av34t5yAUSr6eSttiWfPCQnIKvVPcs27CWra5glPQ+DsLTxA77yFRy+xf0Osse89sb/2XgQXj9Rhn4/o685GCsNHRwPJXDm9peBmte8DrekQ1bjpBU3k7ziujhr+9a2CT5+fmSHSLskvPnz5OQkCDZGMeOHePt27f/Ns6N5WojbB5hUwn7UdzvuXPnEHaVsJNEfYTdtNg5I5/soKXkLlFaG9D2uMr9Dy1L2TbLXey/2/7jnsCqgYeZmRkJaRMI1t9rSMsYbftE8d0wzPbp4pP8jsKGZipezIVauGVSlP+Yh5fD8XWwxtzag+DY08QnpfGkuI7GgSnksilkY21U513m5GFLrISX7MYHWhfHCMoFdXVaov9IdKZvbyi54Ma6tU4kFjVQN6Yom5EGtfKRPO6EOuDudIzT+UJ7FZhqo+JFKun3snj0dXAh1W5mgKH2z+TeuUnKrZvSAEYMYhbkW/d59OozX3unQDZIc1khXyq+8717AtnkIDP1T0m5eJ2MN5XUCjWuH0nOSMUTHiX4YG5iioWBGuqa+hhZ2uPo7IijvQtmHtfIq++hd6CSopRIXNU2s36PHsbW9jg4O+OslB3trLByiOD4zTdUKbxvEvBwBE8HT7xSvkvxleIOZkZaqXtznfOHddm+SQ2Tw6EExYiQFpHjOBkbzbHoKzx4X039mBwh2jmU4YaJhsuywIN8pI7PmScJMNRk9+E00lNOcdLPBG2VLWzYrIqGQyjhJ06TcPYsZ88mcuZkFJFuOuzfvg3VQ3Y4BZ2YKzvP9edfqW4fVTLsfjww5JP99Dd84ElyMk9LGvj2IZ2sE17o6gVxp62Djx+LKH6Tw/2LkQSY7GajZTzJ95+Q9+4zpRX5VBQmYr9BBR8F8ICM0a46iuIMcHTzxSc8jvhIX/ws97NmlzVHImI5GRtIgMcRLGyP8SD3HL6W6qipmWF/LI3CnlmKnnyskpxz/gR6BBL2pIOJBfRZAaZMMiYBPgN01uXz6v5FYqMSuPuxnYbOBt4kB+JnZ4l+1HNa+wYYEADRsnmIoeFRiWI7/1Rm18R7LRgKAjQQncO9e/ekju/du3cSoGBoaCjRoW/duoUIQVCk/v5+3r9/Lx0nOkcBZj169IirV69K4IO6ujqOjo6kp6dLHc1i0EJxnv/LZUlJCXFxcRLIIMACUWfhXRAx4hoaGujr63P//n3a2toW3KboPAVII7I4TrBDrl+/LtVbHGdtbU1aWho9PT0LjvvbfsiH6ax5x+MzIYQGBRIYOJv9j7jgbqHLfn1bHDx88A1QlAUQGHgEa+2d7Nq6k11aFrj4BxIwd1xg8GkupLzl69RChtXs/QsWzhidZc95lvmErJdVkqjvEjPzN4GHmd5P5N04SZR/KDG3s8l5+553+W/Ivn2a06HeOASc537eG169fcydM0EEuDhhG3aDrJdveZP/hmd3znAmxAtbn/Pc/9xOp2DMreqB/1XAg3gu43R8SOV6mC1mOtpoWAcRnZRKWvYr3uQXUVRUQMHbFzxLv8a5cCdsjQ3RtwkjNr2M5tHphXHssinkQ7UUpMQSZm/EoYMmWPmdIPFWBlk5QjtGcb48crMzSL1+nos3ssjIb5IGYT+tu3ycti9PuOa0ne0GPoSfjCfaxxoL7f3stY3m4tXbZH9sprFniJHeJmo+f+KzpMVQzKOLIQRYaqNieZIHrwsp/Cg0Gj4j9Bca+gSFX5C0emn+9Jh7sUewOaiLqd8l7hU20rr8KHfBf2nyUzIXvJ0wcojnXtsMY7/wJM8DD/sx94ri2MVrXLu2inz1DHFhzpjt/ruAh2m6PqRyJ9AUFbUjXPzUTcsCtuFfCDzMjDHRW86riz64G2ija+SAW9QlbmY+J/dtIYVFRRS+f83LJ6ncPX8UX7MD6Bo545nwkNy6xYLUcqZHOuj8dI8L/maYCMDB3p/wxJvcf5zLy3fifIUU5r/hdc4THqXe4PKZM9zOq6C8czXvnBCOfEz6cSesbRxwcnbBxWVxdsZcexe7RdjjPgNsXFxwXrKPC0521tj5XeD6i0qUx7nTA43UvEvl6jFPbA30sPFxwVB9N1omqwUeZpgc7qDl0yPuxDhhb26LY9Albr2tp29mFtSTj7Xx7X0Kl0Od0dc2wz3mBvff19HYpyyAvqBp//0//gngQT70mfz7Jwk0N8bCTB+NfRocMrHGxskJZwcHrG38OP++nfqBfhpeX+WC4z7WbVRFx8wWG8eFtqSzoy02Vo4EnLrPoxqFMfmrasuRTY3QlOqJm7ETrnGZ5P9gEP/q2OXLlx2n/Ax4mB5mQOistfVLTp+l30450+MjkrDkYHcj9SVPuH3Mn+gbr3lX2UJLSSaPomzYsN2LpI/fqOlfyQ4bZGhomKHhMSZly4uBC5tKDNqFTS2AOfFbMI3v3Lkj2WfCISJ0cRYP3pd/Ev+7W4UtWVdXR2xsrGRDCZtQ2FQCSBH2kLAxIyIiJGfvgjuTTzDYWMhz/72oaXtyPOMjtcsxCRcc9N8f/4lP4KfAgwAbxABGDEiEp/7p06eScS0akKDPCFTubxlIzPTTU53Ndac9HHI8RXJuMa+fJM1qPLhlUJCTTPKJYHwDTpCQ8oyKiickBnkRGpPEtexiyiu/8CXnKvGHzbC2csMnIZOXjUrCAvJh+tvrKC94J70Q4qUQ+U36JW4EmrJ2jRF+VzN48Gph+bu880Q6GmBq6IrXpVdzx74l9/Ejnjx6zPOiMqq6ZYwrvrWTDTSXJOElvCmmDtg6H+bIkSM/8mFnc4wOGmEfdImU6lGYaSDvXAhnkjLJ+tzH9Eg7kwUROGmb4X3uGS87lM16GYOF17nqa8y2Pca4RB/n6JzHNsLXGScDDf5Ujyatspna0gfcjXJAS9UAh8BwgsIipBdfvPwREeFS9rXWROOALU7HM/moGFcuAR5kjPc3U/v+LpfDbDmksh0VXTscjvjODmYCvPFytcJo93q27XPnaGoRX0d+BTxMMVCZSeoxS7T26qB/rpxXD86THOWAqfY+tm7YwT4ja8ysbLARIlPGuhxS3cIfa3ZywNAUM2ux3QJzk4OobfyT/X73efipe0Wvv1wmY1rEmslkTAqNBwE8HPLnTstXHlxNJDEqBG9nC4zVtrDmgD1u3oGEHU/mZmYG+QWJ2C0CHkY6q3kbsQ8rn9OcvpPD64zLJPsbsV4zmLMPnpP39BIXInzQ1/Hk0ll7LO2tOKRlgI3bUU6+EKJUU/RW3eNikAcePoncrVZoN8x9TuQjjA7UUPgwnYeSV+Eet29c5WLiBW7eT+N+2nVO+lhgflCb/U5x3Lmftoz3YZ7ZkpH9hpdlnUvicMWsHpcuXZLCPgTwIAT6FKm0tBR3d3fWrVuHmZmZNAAXQJ1IIjRBePoFu6GoqEj6JojtokMUqLyxsTEbN26UjEpxnmWNAcWF/g+WQpshKSkJPT096b0UHaboOEUSHbsA5/7xj39I4In4Rogk6iC+iz4+PggmiIglF99KkcT5BCBhb2/P2rVrMTAwkEI2xPa/PckHaSt/TkqoA072ttjaimyDuYEOB7av4R9bDqBrbI61tF1RbouN0QHUVLayeZsqB0xssVGU24cQdekZn5YFHuZqI59gcnKKiYkZ5DPTTE1OSsCUAKekPNbLRNMtoi21sPU8z6X8wYXlk5NMTc3HhctGW/n2sZD3zx6R//YW0X5+hIQe5WhkJFGREYRHRhMdEyOxTKIjggkLDsA3LJro6NltMcfCCQ/2w8sriMAbJVS3Lyegttx/4i8CHiSg4CvP4hyx19HggGUocQ+/0jyyCFCQ4JBJ+qqfcz/WBSsdHVSt40mvHqB7QvGdF2EtvXQWnOOotTY6OhbYH71JVmkng9MrhMNMjzA2Osrw6C9G6uIRyHqoL7pDnP56VNT1OGRkxKEDe9i1dTMb9x7CxNSasJvvefW1h76eHglAEyBaT087JWlxRDvOajy8+9ZCq4j1n9unt3dA0siYGqrgzY1IfI2NMHSP43phO62Dw/R3NFJfXUtd1xhTMhkTQ7309vRKmgWKmv828DDYSPfbc/g7u+EdHEnUydOcPr2KfCqW6Ah/vJzdCE6ppOqvFpeU91KeFU+kxUFUrK/wtnOEhUSavwp4kDM91ETr+0S8tXewX8cBz4RHvP4+q4e1YBAlm2Bq8DufUyPwMd6PjqkPgTc/0j4nuiy9HTMj9Na+4ukxYzR2qqPvcpyzjz5T26swDha9Q0LfZ7CfkfFJfjTfRbss/DlFfd4VztrvYu0uYxy9/PAPCpaYYoItNpsDcTbch/quXezUtORwUDCBP8rEPkEE+nvjoL2FXbp+HL1TjLIe4WTLW7IvBOJsbIGlXwKp+SlEORpgZroK4GFmjKHOWspf3iU51BIDLT2s/S9x83UdnRMy5NPjjI6OSfosU8NtNBWnct7LGH1NA2wDE7n8pITKln7Ea7jg2Ss/BNkE44PddDR9o7a6ksqqamrq6mlq76FviYi4nJmpMUZ6Wmj6VkNNVRXVNd+pb+2mb05j4Mep/xngof8tT88exkxFHR23aMKjZtl/x8O98LbTZ9fmQwQ/bqTs20deJofgprMfbacgAkKE/ahkT4aHEe5riYGaOqZHErn6ZUqioMrHO2mp/UqpmE1i2fyB4sK3ZESaYKJthlXAOVIKV9p3bvunar619i7jFZcxMdxLc2k+nyqb6RBaKIp/worAg4zR5mLeZ2dx60EhzVPyhQAwcuSySdo/v+DV03TJwXI/5Q53Lpzm/PW73LqXxp2zERy102btFgv8zl/jqmCKLGGCzG1Lf0TmkxLqBsdZPLYWtpMYtIeGhko2yI//K0h2h5ixRzh0Ll++TFNTk3Lxv8W60DASDhgBjohxo7LtI+wkwY4VGm2CUSpsrfkxpJzJnjqabtuht0cX18Rn5LYoBlP/FlX77038RU9gReBBGNeCHi2o16IBbd26lf/5n/+RjHBVVVUJdRMDCfGSzDecv+iuxNSB/Y20P/TGStscn4v3Sbp1YV5csrmJ1rYO2jv6mZCNMzNSx9skT1z0dNAxtMYlwBv7/dtQ1XIg4Eoe+c2LxBanayh5dBIfjb3s3Tuf9+zeyc7tW9iwcRs7du1m9575Mmm/PTvYunEt69duYOP2PQuO3btXjYPmhzn6coJ2RWzwxDcaC85g/+d6rMPvkJw1i/oJ4EYgmO+yjhNobYqNx2mufh2G6a+keOniFnCeC686Vgc8+DuhYXmKZ+MwKFltMnrLs3kYYcoa9Wjuf/lM9s1wgm2t0PdJ4YvQKVR8hKV/l/gxQ0tGIH72HrjEZvFJYVsoAw93a5mc6udb7gVOuBmhvWsnezWNMQ65x9OyFtqHRhntb+Bb/m2OGe3BMiSFjI/t0kf1Z4wH+UQbxTdD8Tu4kQ27dDG+/J3azkFG656QGXcEfQ0njr2q53NdM83NDVS9SuFesDHr9vhwPreET43NNDd9pTT/DtHaazEMSpsDHmTIRMygEJNSEsLq7RtkeHJ2VovpH8BDAHdaqnl8L5mrZ2IJ87LH6sBW1mq74xt2jLizKaRlP6JoGeBhtLOW/GgtbNxDCD5xiSunwgm302KtqgtB8Re5fOEoEd6emJhFkv7sApefvePuuShiXK3Qcz3D45oGchLdOWzvyZEzz6mcXGSozLTSWX2LEDUVieEhtEgW5K2b2LD2T/78xxrWbtgivadbF+8jfm/dwqb1a9l4wAaT+AKGFg1chDffzc2NLVu2SJ2aACEUSXQkwhD8f//v/7Fv3z4JcFPE6YlBt5qaGgcOHJBYEsqsAPFdOHr0KLt27ZKYA4Jqp9wJKc6/0lLsu7IY0dKjRKcmBru/8z0aGBiQgJM//viDnTt3SiKfor4i1dbWSmyPNWvWSCKjAq0XSYgkCVBCgBUHDx6UDAARbqK4rvhuio5XnFM8MyEc2tXVJR379/6RMT05ymB3hxQaIsJD2tub+Pz8Bgk2Kmw0PcO9N2V8bxPb53PzqzMcd7VA3zqMM+9baWxVlHXTMzAiqe+Lr4RsYpjhgd5ZcUSFWF3/sMSgEcKMk73N1IkQD+XZJ75+pOr9GfwN1TFxPEbMg7JFIVnV1DW00j40y/5RPB9ZXyVNT4LQ3bIbPVtPjoQekyibgrb58xyGv4cVxuq7UPF7TH5d34ogpOJas8u/BniQTw4yUXaOUDN11LQ98LqUj9DxVQyoF15T/Jqi68NdbvodZJsQrLteRXnHnLdUPs5YdxmPAzTQVdWcDTmr+gvFD6e+U5mXgK/qDvQOnyDy5EnC3M0w1lBjl0Uo8QkXSct9y/O81zzPzJBYPYLZk5GRRlKkO64G6mwzCObc7VRSpe2iLIuHT17xsXWcgd5iXtxKJDr0Iqnl/dL0nDNjteSnJnAiKJITOc0MTEzS+ukZz58+40lxE8LJKdra6oCHWQ/kyGA/fd1tdNaXUZT/jrdK+kKSHtEqfr99V8AH4bXs6KFvYIjh0clVtpul/9EFWyYryLvsh5OOIVqR72geXKyp8FcBD9MMfi/gdaQWuzbvwjgyg4flAyvXQcyUNFnH85OO2Gtoo+uayMOOeWaJbLia8qexOKlsYJtOKBfyqmmand96QfX++R9zwIO7Hjtsr1PYOaykaSTOKlrBr8QlZUyNdvEu5iDm1gFLgAdZTwnvMm9z7nwm+R2jjA/ncPmIEaYrMh4EW2lG8moPtpWRfy+WMAt1tm1TR8/7OhmfW+kR4+ipUSa7avjyqYJaMe262DbRz/C3Z1zzN8FIbQ/7jQ/jdymHjy0DEogwNaMsAipEZycY7a6l4k06txOiCPHzxtsngKCIEyTcyuZFeeeP7674esxMDtLbVEpRxiXOHgsh2M8P/5BoTiQ94OmXNrqGJ+edCf808OCJvZ4zQbnj9MyNhmXdH/l8L4RDWw4R+qiGtsq6SwAAIABJREFUN0+SuOBrg75FGBfLJxhYHFYim2Km+R4J7lY4e53lWqlg8k4x0/KUO8d9cDKe119T6LD9WBrqobVrA5vWb2LrLnW09X+yr5ERxlZhxKW8n2foKhqjfJSu6pfcclbFxP8mWZ9bGVJ8gFcEHqZoeBLLcQdjtK3jSGubYugHWiFOPDvD2rt4C5x0dyy0wSR7ayubN65n3Z9/8D9/rmfTlq1sndOIW2CvbdvK1s0b2LhhJ+t2BXO7tot2JYxYjLuE80M4PKKioiRbQ1EtxbK+vl4auIvBu9AkW20S5xb21O/YVIr9xbGrTeL+hZNQsEiF3tXiJGxEIf4vWLTCDhHXUCT5aBvjJSdwObATk4Bb3CoZ+knfqTjqv8v/tCewIvAgDGlBL9bS0mL9+vU/jGhhSAsPoPBiCi/nYkTrr3oA8pkhpntfccHVFO+oc0TExhLzY1aLfiZmRPiDUNvup7f2HTk3o/G31ERt60Y2i9kPnI5xKecrVZ2jTC6WJ5d101L5lifXLkveTuHxFPniudOciTtGeEwC5y5cWlAm7XPpKJ7m2hzUMMDI+zSX5o6Tyi5f4ca9h7yom2FI8TGWgIfT2P/5B6r79dE1FgJcStlYk30qahi4/33Aw837Jwg7bI6ZbQiRT1skISrRpc8n8Wua5gf++Np54HriIZ8Vzlkl4MHzWjGjVVcIs9TDxCaEo5dSeP7oPEHG+zB0iCT++l1Srp8g1NYANcuT3ClqpGVkdk70lYEHGUNfbpBw+BB71v6D9QrgoXtCmoniyWkvDHWOcLp0jNZhERPcQZWY3cRKi+3uD3gnBBhFO5jqobPmMWcOrcM4eA54kA3Q25DLFUcdTHUOsH//fvYf0MXQLpizH8fpGJXzA3jQC+VO+xhtQyOMDPfwNe8GiU772XIknXeVrfSL8ISBauqLl4ZaTAy0UZ0ezamYCMLCg/B1Ncdwzxb+2GqIrVcgwRFhhEXEc/zCU8oGh+gdm2Cks4zC1Bi89FVRt7RFd48aln6XSM7vXCoUJh9lbKCOD08fk/1wdvYVMQPLj/wgnlAHPfQ0TLGMuEN6plKZ8n6ZN4j3tsDCxA7T04VLgAfhrRSdwe7du6XQAjG9qCKJjiIgIEAaRAuQQTAbFICjCKsQ3wORxSwCokw5CWqg8PoLQEOwJhRsAuV9VloX06WKTlYwq36VxKBf3KcA9H4HfBD7CkqjAFb37Nkj0f8EGCGSEEIS7I8///xTei4iXEQkUXchlKStrS2VCSNBMEQUrAexFKwHYXAIsFaAL6Iu/ytJhI+J+dCl76NYjtFUnMEVp11ssk7iaXkrQ9PK5TOMf75CoqcNxg4xXP46wdiUolyE+SgM5hk6885yxtsEDfEuzWVNl7PceFVLT2sptbc80FHbw66dOyUQRwA5O1VU2LljE+vX/IM1azeycavKfJm03250HUKIzh1aKBYoAQ/B6G7VxCnqCpezVjlLQe4j7l44ipfubnb5/28DD3Kmhjuou26HtcYe9DwvkFw8uPLgT2oQcmb6Sii4G4Dx1h3scLtHXmXX7Hd6opXeiqv4q29hr34YcRlf6FAWbfwXG5S8/yPFqUexUjUn8kUjX1vryb8dSYidGXrRr/ne2c9QYzb3Yl0x2CEM6fm8WYggr/kHf6zZMGdkz5Vt281eTTsiX3bzXVCN+3vp7BxkbEqGXD5Dd0ESCZ7WmNhHcbF0jJGpcT7f8CPI2x+vi/nSVMvCxP018CD6rSlqsi9wMcwZRwcHHOztsLWZZcYJw/d3s62dPQ4ODjj5niT+XjHtsuVCjH7vocu6c0mPFew9axxuNNE11yfOn+UvAh5kPTSV3CNefwtbdrgQ86iUqnkFy/nL/VgTg+xJOl7Ec8JBE1U9D0JzBukbF7PTyBmpyib7uBnbN+/B9EwRhQ0j817jH+f4V1b+fuAB2SQTY6MMC0r7zCTykWwuHTbE2HgFxoN8ihkRoptziZNHTDikuoe92va4nn9FSXM/g5Oz4Kist47uRwGY6ToQnlpEiRBTkQsWxCij3eW8uRaMr6k6Kir7UTcL4Pj9Esrah+f7djGlY3Mu96KdsdLYhcpOVdQPGmNkpIeO+h4OWATgf7MUIU8sDfdkfTQW3CXZx4B9O3ahdsgccxtrLI100FLdxy4tZ45mVVLVPTdl8N8BPGzWIfT+Q2IDHHE0d8bhZK40a4OSvutsY5BNMtNwl9NuVjh5n+OaECyRTzPTlc+zG2c4HrxQRHZeVNaPAD8njPdsZMuaf7Bp70EOOfovEZ2d3z+IoIhL3Mr5QqPSwF26iekWGj4kE7h3Iwe8U3la2jkv1roi8CBnojaTlEgbdDStcUtro21oWomtMst46Cx7xfuchzxUtq2k9Qckx/riob+H9bqBnL5+j7Ql+wjbTMxsd5Sj9ofYsCeUu7VddCjdvxjgC5068e0S9pQQ616cGhoaJLalYFz+DvAg7BrBVBc2lbB7fpWETSWAg+/fv0tMi1/trygXx+jo6GBlZSWxXheDFgJsEGGsywEPzPQz3ZtJjNFe9B1PceZZ4xKtM8V1/rv8z30CKwIPgiYtwgIEXViADcvlTZs2SUIhIhTjL09i3u+ZfhpKS6ioKiMv7RzHlYCH3u+FvEs9zTF/d5yshFiOHvomdjgHxHE29QUFpWUUvc3jbXEl1a2LUDP5FBMj/fS0tdLaOpdbKil8eJGTHpbo+Nwl91M19S1K5a0ttDa94vZRB5wtXXFLKqKptZWW5jI+vBWzPHygoqmL3jH5/PziPxgPazHzu8DpG1lS/LsYrIn88EYoR0yNsPxbGA8mrFE/xrVb4YRGRBGW8ISSruVUYmeBh8b7vvgI4CHuEYIdJyUl4MHr9lcmBsp5/UhoHnyhuqWD3rZyPqWGcthETxI109Y14pCZL8efVlPbOy5NLyXOsyzwMDOGfPATmdF2ONsackBLm0OqupgIxkP3BNNNz3l62pkDW1XRcfTG3csXXx93HM0PcmDbJtarWWDndkQSzPL1PYKHmE5y659o+M+FWshHGO4qI/dSDKdjoogMdsXZXA9NbSci8wWQoQQ86Phwq/Q9aRlppNy+TmKkN666O1hvGMzxxMvcvJ1KVvZ9nmYnLAq1YJZV0fmdmsIs7sT74mGizt4dm/hj3Q7UDJ3wjL7CzafFkvDV8Fw8n2y4gaqci8Ta7GXjps2sX6eKse9FbhS0MKTUCc3+EwRzY5zhvl76enslFpLwqCtyV/ltznqL6eb8iXzaSmd3z48yxT7SsquUpwmeeFjZY36meAnwIDoi0cEIGp8YvIv3QpEENVLE3woAUkx719HR8QOlFgJCIqxADNqFgJDo2JSToBoqwi2E1sPvAA/i3IJOKJgGPwMfRAcp2FdCV0LoTAhGgoJ9oHwvy62LTlHcc15enhQyIdgOCgRe1FuEKwhQRdRfMJVEEs9KoPoCSBHggxDiFIwG5WuK84mBtwAeREyjuL//mzROc0k6SY672Wx/nRdVnZJXWflepkqvcs7LFmPH4yRXTTOxpA2KvWUMlD3l8fXTHIuMJDzYH3e9bew2j+bsk6/09LfQWZTC2VMniTtxghOKHHuM2Ah7DFS3s0/LAkvvmPkyaZ84zt3M5GnVOMIrqEjzjIed6Fq44uIXIj1H8Sx/nv3xdDJDf9//BeNhmvH+b+SFHUBvz0HsTzwgp2Pem6Oo25LlVBNVeecJ2L+VTTrHufOhkS4hDNlbRfNjf/S2bkTb+w4pxSvMHLLkhKvZIGe89hnPznihoRPK3e9DdI5382mRuOTMWDtNX4t58zxbAtsE4PbT/CyHnJcFlHVMMCymCZXJmBHe3plxplryuBthh72tN17nX1E9NMO0fIKPVzzwcjmMS8LbH4KYs8CDA3pWR0n63ML35rm+WAgW9w3PeYOn6SjL41XmDa6vRs9hlftcT83mWfF3BuRzA7/VPM4V9pn+9oCboRYcErN5ZA/Rv4Q18BcBD5PfqMo7jceOTWwyiOduYQO9y77HyjcqZ6I6lRshphw4YIllUhUdw1PImKLl7Q2SXPazcYc1Ua9aqFtxfl3l8/3O+v8C8KB8O/JJ+CnwIGeqr57m7Gh8LA+ic9AcG594zmcVUt4yyIgSQ1DWU01Xmhtau4zwufmegh/CEqLBjDPYVsWXF7dJjnLHSksdDX0PYtI/Udoj4i7GmRquJveUM076+pi4hHMiJYf3xR8pKflA0bsXvHpfQlGNgqklvrsZpEQ5YqZvhkVkCpmvi/nw+TOfCp7xMDmKw1q70bQ6TvKbWppF+/pbgAdtQlNSifAPJvJYEne+9CzjxBLdxCQz9bc55SqAh/PcqBDfP7mkr9Xd2rCskKwQlq3+WkTpmwQ8NXawe+0aNms74XDyCaWibJHw7A8h2tomWruHFjF4xXfzC2XpIehv2oFVYhEFDUqM5xWBB5CP1FBw9yieelrsdU8jv3VoIQtHLmNqZEACUxfYVsIe667iXUoMgSba7PRMp7C6iU4lO21+/y6+v73L7QBTNuwNI2UZ4EHBeBBMUW9vb8m5q2BUCvupsLBQYpAK8W8BQqw2CeBBhIuKEIdf6UMI54oANUQYrdC7UjBcV3MtYU8J4EQ4scQYUvQXQgtMkQSLXjiyRN0WhlqIpjLCzGghVxzV0bOK4FhaBf8586Ipavjf5a+ewLLAgzAWhAEvQiqWAxyUt5mYmJCZmfmr6/zT5eJe5LJFs1q09DPw/SU5yREcdnDGxSuUyPhLJN97Sm5xDfU9Q8xMt/HuShixCbe5+65pHvEUdyKfZGywi7bvdRKdWlCqa2u/8Pb+aY7barJeJ4obecV8rhHba6mt+0ZdXQMdfVU8PuOJt+Nh3K9XIiSUZnryeXrtHOeTMnlRvwhF/MF4+JNDtkH4Rp5aEHd6KtIJS52DmP5LjAd71I2juNfYTq1EoW6l6nUKtwKMJODhdm4Wj94WkPPiDR/fZ5P6rJLm/nHmHWezwEP9PR+8JeDhMWXLAQ93a5ic7KWxspSykvfkv3xM1u3zJIQLpWcrbGyssba2wtTUAY/oS1xJe0ZuYTlVTV10tzcwuFhccqofWUsqsa5uBEWHEhTmgb3aPPAw01tO6fOrxAYHERrqj5u5Fhq7NrF+3WY27TyIlU8YgUpaFeFhoYQG+BN9t4iPjQJommZ6vJ/22gqqKsopfXuTq5HOGGk6cHQx8KB1hJsFGZw5fZzI0BD8vdxxtTHDyO4IPv5BhIRGk3D5PLcyzigBD9PMjA8wIOiPz9O4dTaW41HRRAR4Emivxbo91hwODCE8MopjcZdITn9DZXMVha8e8+DqaeJCPHG3t8XO0w93e2scnDw4Ei7E/LLJKaqjZXB83kPyk7dn/PNlTnvYYOEaz7WqKaYmJpmWLxP/PVPPm8t+eNk4YJH4YQnwIL0Wcrk0aBchDmIQLd49MXOD+BaIkAIRWiAE28TAWzHIFgNu0TGJcALh1V/cQSUnJ0vHikG4CNdYDdKuqG5LS4sUviEoe4JtIM4t7mlxEl6BK1euSCKPgoGlAA4W77fSb3FOcV/KoIgAIESdxDdQZLGuCCNRPBcBLgjqufBQKIeQiHUhCCVYYQJ4ELHmiwGZle7lL98uH6ah8B5nbfaw1fUeb2q6l0x7uTrgQc5kfyut36soLyul5H0Otzz3om0TTeLjCvomx5joa+Zbbe3CGQoqP1Odn4C/0X5MnY5xPL18YXlNLd8a2+hYMdRCFdPDEYSfmWWlKdhpKy4vJRAb4oaNxm52/jOhFgG6bNykjllIInHnLkvtSrSthfkyZ2OC8DNUYd06M6VZLcYZ6f5MqutOtHZb4HUxhw8L49qW//fK+mkoSiHeYDMbVbw4966Ghmk5k62fqLxszd6NWzE/8YJnNYunrZMzNdJHb+t3pX5M9FffaWjpondcvuIUw2IwMNn6mc9P73DirGBjTTA6sxR4kIlvyXLaHXMaHuMjA/R8fkRWWjYvSmppHZ9kUtLsmCXLiwrLp4YZaS3mxXkP7PXNcAi9RmpZDwIGlws6/UrAg5cpe/bqYxVwlLCIoxJzKPJ4Elczi6kX43Vx9GAnXa3fqfz0nhe3TpNwOn5O5Fghdry6ZfypM5y6mE5OURmVTR109I38mD1q+X/a6rZOld/ksp8JWoZeHHs3yaCCDfnj8HngwUFTjX1GRziWdIVLS9rcXBtMiiXYxQgd1YWzWsiHy/iSFY7Zxs1sdbpFdlnHqvoPWWcO6Scc0FU1RCvqHc0DE8jkQ1Q+SSTaZC+b9gVwvaaXNoVNIN33rLDsQKfQG5izkSQbqpa6+jY6+0eV7IsfFV208u8HPEwPCfD0CrGhUZy8eJ+HBTXU986xCJTufmXgQbGTjMmhdlrLX5J96wzHIs6Q8qaaukEZQutgqOIGoYa70TUPJOJWAVU9SteQTUo6OeOTop8T6GMnH28G429hjK5TPCllPfQJ5oW41Mww/bWveB6pj/quQ3hcyONN8+S/BDyIKdm9HjTytb5NosG3fc0h97IvOpsPEpL+ntSHr3mXl0t+8TsepudS2j7MoDLtQQAP324S72KFk88Fbn5dBfAqajrcRG9+DI77tTDR00LX2hO7o5l8HZOvoi0pnrv0UBiszOZpjCXbt5hw9Fk91f1KCNxPgAcx6G0tvMs1Lz02qR7mwvsWGlchhCtdfbKK/LsReOobon+mjPruYSanZUwrXXr2Lmfo/ZJFeqgZG/ZFkFrbTafSPsKuEiCDmPlB2Ezbt2+Xwi6EU0XYGcK+EcxUMXuWcMwstreUn8TidWHfCBansEfEOQoKCqRrLd5P6FcJ5qoIaRTghtDxUrZvFu+/+LewCYXDSHH/wnkjQncF2CHOK5wSQr9CaD0sSfJxZBNV3PPUwMDMj5CbH+ib90cs2f2/G/4zn8CywINoZGKQoIhRVgYaFq/v2LFD8nT+rdWX91H94hLHrTRY557Fl/pmOuve8PJBMqfik7ie8ZJ3X6qp/lYvIYANDTXU173lsrsmRtZ+BCblUdvQRGNTFwMT00zPdNNc/pLMC6clyo+g/Zw6Fc/xIHc8DPfy5wZd7IMiiYwX209x6nQipxOukV1ewYMEP/ydXbA794FxuYz+4rPEuFhh7ZnIja+zonQ/nsUP4OEfaBg4YOMmplLz/ZF9XAzRUT2AoQQ8zIlLng3i1KUMMj/1MjXUwvDLQOw0TfE+/5xXy4pLmqGiZktAUjLnk5NJTr7CuWh/fC2058QlW+iYGqenKo+cs0c4ZBHLrbd1NA5OzsVOzQIP31K88bLzwO3kE2k6L6kOSoyHw9e+0FOdx5OUy5yPjyYy2JsjTtYY65vhFnGJq+mPeJx5g6QYP1ytrXDy8MU/Io4z17N4+CKftgeuGCnPajE1iKz9IdfO3efp60wy70bgqq4EPEz00t1QSmHuQ7JuJxDpaoKpnhZq+w+ic8gRv6svyP1QTkV1NVUVX/icn8OjG0mk5H2lsmN0Cb1Z1vWG3CQ/LLSWAR4kxkM+aRn3SVk864j4fes+WU/TyF7AeJhmqu879a+vEO3lhItrAJFJj3n4MJ3csy7ssjjH/WeZZNw4QZRfAEdCLvK69AkXov3wtLfD3tWf4NN3ePShgoLs21yN8eGwnRUWDj74nH5GccvAClOcKVqX+L/N0P44ghA7e6z8LvKoto6PeS8pqetYquY/841Xl3zxsnHE+vwnhpU8OIozKpai4xHsB9GxiQG0oMyZmppKIQkCDFCADor9l1uKwblgKQjvtOiAFDND/C4oIBB9Ea4hZmgQbAwREqIAHwRYIAb0QjtCvKfKIo/L3dOvtonzCfaWQPpFqImIoRSUQSH0JO5DEUrxq/MIKqHosMX3U4RbiGn9lBH/Xx3/z5XLmZkYYaS/W2JfiM5fyh01fHh8nqNGu9jueounxTW0K8rmlq2vE4md03hIKGijpb1z/hzdffRK4lyKkAtxdzLGeut5G7kfQ4eYWeBhJePgN2e1UNR9lvEQjO4WNawD44m/cV8K/RPhfz/PN7gY64eL9j8ZahGgzdo/N7BdXQsN7YMSaCZAt8VZ68A+VLdu5I81ZkQoptMULKuOAq7Z7uDAXnsCrr2a/5YqKrbcUj5Kc0kGFy02sXGTM/GvqqidljHaUETJKSNUNu7E4fw7Xi/hFMvorysiP+0sJ+PipJhaAdLFnTzHFcH665hhailO9+MO5FMjDPV2UN/Yzag0FfE88KB/4j3N7W30DI7Q29lO+/evErAowMX5XE5pyXtenrHF2siFIydu8ai0isqaZrrHxbXlyCYG6ftewNtboTiqb2LbXheCrr2lSsLoRaP5GfBgyI6tqmiaWmNpLYBta2ycw4m6nEvltAgQVKQpumvekRkmpnp0w9M/RAIpRIjTbI7A19kCSzNzzOw9CfmxfbY8PMgHH0cL9B3Okv6paX46acXp/4Xl5OdkLnqboGnsS1zRFEPziP/cWZWAB7WNrN+6hwM6wuO+tM1JbVBHg327trJ5h/aC6TRlfR8pSQ1Ab+NWyeOaV9mt9HxWroB88C2PzrhisPsQe4Oe09A/zoysm8/pcQQf2scW7WjSmwcWPRNxz818eZbCrYST8+0uLo74ixk8+1C/YHaJ5a/+7wY8CIRsgpnxVqorGunsWxk8+TXwMFdj+TSyyX6avrXQOzjKlFzOdE8NLQ880N+1G9OINDLKF7FxlR+WfBr5SAF3Qywx03PE/mQuTWMTjI5PSCC5GEiOdlbR8sQPk93bOOR/m5QPff8C8HAYiz1aGAclkXhR2JLJXEmMJOqIGapz4pLl3VNMdJZR+vA0rkb2RN0r5mPrMOOK74xskunaG8Q5zwIPt8SL+ss0zVhbKaXnLdDe70hIbBihYX7Y2h3lcvmQNIXnSt3LklPL+ql7kcRpWy026USTVt1NlzJo9jPgQQCZLYUUXfNEa9Me7C8UUti41JZcck3RI3a/4enZI1gftOZIej2fi95S/LGSr02DC6a7FTZbz6dM0kJM2XjgGA/quiV22+JzCqeGp6enBDwI54XQmBNC1iIL9qUYtAuA4neTaDPClhH6EYmJiRL4INgNiiTOKRgVokyEmQr76necReI8gnEqWCkiVERFReVHqKoIkxHhIcKuEo4cYWcuSfJJZBP1ZPhpYWjiReDVfDoVbWvJzv/d8J/6BJYFHsSAQVBhRINfDDQs/r1582YJQfvbHoAQ+xlt4lNmPCEmGqxzy+Rz6SteZ93gYvxK3owTnDwRhrPODvbuN8TQKZiT8ec4lfiQYiEGNPmdL9lnCdWfi/3fr4bqrq1s3bieteu2oKKqhqqaOurqIpZZLDVRVbcm5lkZKedCCXVzwDQ2l4GZIT4lueBm7ozbycd8WhyK/iPUYiNu596TU9FNb3cnnW2Nkmew6kU84faW2BxJ5Ga1+DpOMND8leqqGqq/t9BWV0J5si1G+83xvZzL+x+UPvG052a18NZm7ZoNbNm5E5W52GqVbVvYsmUXfwhxycp2umRyJrvKKE8Pw2zHVgwC7pBW0sqgJJ4jPulT1N05gqcAHuKfUqH4UP8AHo7gejaPr1cdsDM2wMDKHe+os1y8lUHG7aukv/zEl/pO+oZHGB7qobsyjyfXTxB52ApLKw/sA5L4ct8VPWXgQYhbzYiQgTFGB0spTFMCHjoGGe5poKY4m/SLRwkwV+OApjH2wdGEHw3AS1+d7VZJPCpvo3N4hMHWCsqyzxFkpI71sUwel3VLVFzlNvlT4EEvjDsdk7R0d9DZ0kBDfb0UBydi4errG2hqF6rr1TQt0niY6a2h4VUSgcHnSXldRU17H11Vb3l/NRRzrxvkfG2iva+Z71VfeJ33nubmXO5euMTVm0/J+9JA5+AIwyMjkmZAf1s15a/ucT3hOAHn3lDRNvQLj5WgbfZTkGiPi5U77scu8/pdEp7qqjhEP+Dhl05GlMWRpmvJu+CDl40zDpfLJeroSp250CsQg3k7OzupwxPCkeK3eB6rRb5FByeQcqHvIEKyRKfT2dm5KtBC+f8m1kXYh+iozM3NpZAIMYgXnaFQdBYDLdFBivAQBSCx+PjV/haghhioiHoLDQMBOgjPgwgtEddbLeAijAZhHCh0LQR18u9PMww2lVGa94B7KSlSFuBJSkoSCZHuWO3dxhbTCI6fu8Zdabsom813Th3GxVALdR1bjiSmcPvufNm9jGc8KfjOwLRMCcz73wIeAtHdvIW9Bw5y0MhMmlFFzKry82yEvo46qio72eH7T2g8BOiyYeM+jP1PEBWfKIFtQgNkYU4kLsIHTwMV1q5bBniw28EBVQcCrr1eNfDQ8jGTi5azwMNJATxMzQIPxfEGqGzcjeMFATwsNuKnaXl3i2RPDXbu2CEJoG7fvJ71a3dwwPooFyqmGV18yE8aony6g5LUaMLsjNEJTef981Ryiqp4+Sid9HhPDnt4SMakMCgV2d3VGUf9nahs3cO+g6bYHA7EP/waz5tH6J2cYKgxn1fJgdju28L2jX+ydp8nEXcKaJC8fL8AHrzt0bMM51JJPdXfGySDubGpnY6eYcQcVfPfr0nay55xw3kr22xOce3hW0p/ACTlVFR8Ji3KCntTC0wDrvGmvIIypfIPOfe4F3iQHdoRXM2vk8RAf/KYfqto6svVWeDByJfYwsmfAw+a+9ir70Z4wlnOLGlzc20wIRJ/B320VZcBHu4HoL9xC7u80lk18DD0lsdnXDFcADx08jk9liDdfWzRiSFjCfAg5iX/TEaMK/bqKlK727F9K5vW/sGaPW4EX3tL3S/b3Rzw4HqQbRbneF7ZSJPSzCg9Pd309LSSl+CGh7k5pkF3+NjRQ8eCfbpob67mSagWxpZLxSUX/KN+GWqxYO+f/lg18LDkLGLmhM+UxB1i3w49vK6+I1/EVK2UBHug6S6nXA5xUM8Nt8QXUrsu/9F2Kyj/+JLirBCs92xB3SGRy7nYocOJAAAgAElEQVSN/wLw4IbB2rWs27KTHSpzOj07trJt81bWbzpE4ONGKntlyCfaaSq+Q5zpLtT0Q0h8XMY3hR6CAB6qr3LCyQpH34vc/jE3+0qVFP6TQTrKn3LZehuqFie5+eoFz1KPE2JpjumZj9R0jy+aYWLlc8mGPvMy2R8HzYOoheTytXt0Idv5p8CDMIVbafpwjXD1Dey1T+J+cfMys2Ysvr6c0S93uB5sh66xF4mfqkkJM8fJKZiw6++pHhThS4o0Q/fHDFKDTdmoE8fD7z3LgnTCfhKOFhGyIMLdhRNDjMdESKuwd4QttVqbRHFlxVLYMY2NjRKjQdg7gokg7DpxPsGqEFN4CkaCsIdW62xRnFuxFLaYCE0V+leC+Sl0ssTYUYStCptKOHjE9ZYk+QSyiW+k+2piaOJN0LWCZYGZJcf9d8N/1BNYFngQjUbE9ojp8xYDDcq/xYsgxOYEDehvS1MdVD5JINbhADt27JeAh4/5t7h6zA0LbU1p+j9NzcVLDTQ11dm5aQ3rhNL/zn1oahmhrR/N/ZoO2gV1dHyYQcVsB13fKbp7lDB7c7Tt4nhUXcmn6haaW7ro7mqhuaGSt3mfaO4foCIrjtjD9hzyTqayJ4/TTtqYu8Ry8lHdkmlxkICHBOzXbOZw8hfym6aY7qnk2/NTuJmZYHRABZVdhtiG3+H/Y++9v6LKuu3v+w+9P37vfUL307ltQ5sVFUUEEYmKigRjA4oK5pxzTpgQFDOomEBJkpPknKpIVZ93rAOFBYKgrddu76oxzqhTVSfteWadvfbcK9ypkkeTlc6KWE6G++A84jeGDx/GsO/+yXcTVrA1OoXCXv/TbuFhqQe/Tw/lWEYOL7NzyMnJIinmCIcCHbs9HkR4kNxHrTSVvuTpjlk4jZ+O76bLxORI7FuX8JB1YhH+HgtZsCWWHu+4HuFhMYtPvKalpoiiknLKKmupb6ikPP8xsRvn4TvdgRlz/Fm67TzRz4uoM5tpbm6koa6CyqpKSnIyqY3yw8leeDAI0528ri21l/CQmXqdc+vn4zJ6JCPGOeG6KJL9N1+R9CqRByfXEOzwE//zr9H47rjIkWP72bXcnRljRzPMcSlbYtLJsndd7Cbm+4WHEE6VN3N7/2KWuIzsnRzv198Y77edHVfukNdHeMDSQUdjBTWvorlw/CB7d+9h987tbN+8gXWRm9m2cxe79+xi184dbN+2nV27t7Fl0ybErXfbDvltjzGgl0H9nt072bljD3sOXeJOQfPgKr+lBUwPObRgCq6ea1h74SVVJS+4vXoK0yfMwGdDFDHZzW+N8/YM4nYuxn+OH/OPZWHqlWW7979XFHDxbJAQBskKLwq1JAKSslnSYQw2wJeOTcISJJZQFG/x8pEObbD9el/F20/S+cnsvYgPEjMooV3SWYrLoFxbcnJyv9mf3x5haGvS+cpsgMzoSnJIaa8836QdIsaIMTDYSwyCiIgII75REslKoiWZAfj8r3byb+9n57wRvZIA/vD9d/znm3/xz//+f0YiwG//8z3f2yUJlISB3/9HEgX+D//9P//kX/K7kZG7K1ngj2Nn4/jHVbKb2u2EsP8t4eEPHH6YwtIj97iXW9V/7pK+cbTVeSTfPMC6mb8x/GOSSy5z5IdhroTFFZNe1mi4soo7a++lgTfJN7i8ZAzffevyNtTCKqEWrzi/4JeuUIt9cTwbyq23hVpM/55vfw1kV0I2hR0y+/ac9H2uDPv2e1w33SEuu1cHYFCqta6Mkqwknj19ytOnT7izL5DFThMZ/8HCQyfm6hRu7VvC/LHf8K8fh/Pbr6Px3naDY6cvfJzw0PiKe4dXsnDKVCa6r2ZT4EQmOskAcajCgw/TvTZyuriNpraOtwlTexKe2v5VXcLDUa9/8/1oJ9x8FvXMDnbNEgbjOXU4I4f9xq8T3VjUPXNom0EMnO+B5+Tf+PfYUA59YuGhI+M0h5Y5M9FxIaH3Wt8bauE9xRnn5ad5WtdE7Tuc6+ZgfQq3Di5n3oQ+oRbNKby8FobLN12hFjdSyt8JqbKhZf/eFWrh2RVqsdYWatFA+rVtrJk+nP+MWsnx3DrKegkJEmrRRGX+a9JfCO8eknDvIpvdfmXshAUfJDzsmPs9//3NL4waO57xEyYYycwlobltGSU5K/79b/79/W+Msfve9vuE8WMZ8cM/+HZ88DtVLezbKeG178/x0Gvr9374eOGhk4b8p9wJ+Z3hP7ryx/knJNsqoPV3xs5W2lP3smbuOH76x7/4139+MvpT6VPfLj/x8w/f8M//9//4zm0Te27k/gnhwZ+5E9xYePI1z1KzjTxG2c+vEL3bnwlS1aJbeJDKH+31+ZQ82Iz/5N+ZsXAX++8UdJWJ7WyjI+Mgkd5ueAXv41RmL+L010ostSmkXl7NrB9+wmVTPPG5NVS8vEzUKld+GRvIvidlFDW/lRn7PYjxpYW6J4fZF+DI6IneLImpoaJvMtfBhAdrOw0FT7i1chSjfl/AussvyBg0XM5M5qW1hHu6Mm3hQRIaWyh6uIO13o5MdVlGyOkUI2luV0RFB+VPL3Bq6Qy+ddxBbEEtdW9ViZ6miTeA2DmS7FbsCBEcbAKEeJnLgF4mdoZik/Qc1G5FbCoJI5cwWrGpxL6RyZvQ0FDDrpJJnY+11+Q0cnzJ7TB//nymTJliTEDZxo4yWS3eHHK+d15WE53mV5xcMJZpLisIO53Eh/t1vHNU/eIvhkC/woNco7izSpkbG1kGepdSm7b69p+ybZKMqqMu3agDvtJ7JpNH/sKwkWOMUIvk7Ezys17x4tF94u/d4Er0feITHhkuQuImlPg4nscJUWz0+B2HmX4s3HiW+MRnJD7NoqSp9a3rk7UdWstJj9lllIh09QgmYPctXl0OJ2ztfk7fSyMt4wmPT0fi4xXJhZdveHlzL3sCPRg9NYhjJ/2ZMcmJhVuucCO3xU7V7Eaio5b64kSu7NlPbFIFb4wYvypqcxO4dOwIx4+d5kJ0PE9el1Jj8zJoLSTjYSyXjx/l2PETHDt1gWsP0skub3p73cbhbcKDN+NcN3G9qZ2atnba21upSI7mcpgkl1zHBcPjQXawYmmrp6ngFkeCnHB3D2bl4Qfdma/beX1sAYs8FrFw64235YnshIeA07m0dbYbCeAsRh/QaVQUqcx5RXL8dS4d2cqGlQEs8l/BijMZ5NWYjRhcqyTkqS2msW+OB3uytKf1Eh6yijJIvhvNpdMXuXbvMUnJz3hw/Tj71gYTOH8+nn6BBDn9zHiHKUyYNIFxo8YzerI/G+6kk1rRbJRtsz+8rA8oPKwPwGFqKKfLm4nd5sEiH2/mrtjFmXNnOXfuNJE+43FZGEHkuZvvCg+CqrmO9rTT7Fm3nMBFb2cCbTOC8r7AxwNP5ymMHT8DV29f5tnNGNpvt3DRclZGHONGSRv19nGTfRsj99JUScOTDQQ5OuARcozTr+rpaK2lNvMa+xbPxM1lAUG7bvCi2tIV592ezPUtC1k4eyGLzxRgfo/wIB2ODMLF80lc7yQWUK5TOj8Z6MvgvF+1uvs6xUPh+PHjxqDd5o3wIbGI7zRX6q50dBieBydPnjTCN6SzFLVewiLk2EPxRujvuPbfyTGkXSIUiFeFiC7yfJMkSZIISZ4tA51HvhcjQHIPiEu44CSKvxxroH3sz/3n1y00leeS9ewut+LijOoccXFx3Di1iU2LZzD8p/G4b7rEycs3e36T32WJPbCMgFkOjJ06j2VHbxAjCQK7f7t1L5GHKSV9QnP+t4SH5Th8/zPjHecwZ76/YayIwfLexX8+XrOnMnH4r/z8MTkelk/nx9/msCa+hoLGgYzdTuoy73Nj5Ti+txcekOSS+TwIn4DjyIl4RF4ktrQfq7LvzW4vIOP2DoJG/sA3Uzdy9kWRkWSxszqdwiuBTP7hWyYEnebcsyo7r5Oug1jlmdzaQovhOdVI8fV1hM+dOiThobOhhOKXcUQd3cmW8CUEzndjxvjfGPbLcH6ZuYKNx2O4n15GUckbClMec/96DPeSXpGUagu3kFCLBG5vnMWMSXPxXXuUK/ahFm1FpNy7xrlDZ7j28Dn3Ns9kluvyDxIeZnht5mzp2zKPfaHr+tzt8eDzHT/ODGHTwXPE3LjBjZ4lmj3B05k1dRpT/DZxNlY4bvs9lisndrFzwXh+mLDqkwsPnSXXjUz50yd5svCCJJ+WqhH2r7ehFt5TXXAJjTK8ZAZ8/HfmkHA8hAUTewsPtOeTeX8Hi4d9y3dT1nMycSjhDtCWcZojK2Ywetxs5h7KoaJJMvm3UXT/CHu9R/Htz26sfVBGbk89Qtu1S+leM+aWFpolz0jpC04tGIHD5A8THnZ6DeNfw52ZF7ycFSEhxkyrzLZ2LSuZ7/Q7Y34TwcidgJAQ/uj5rWubP1YswXfyD/w2ZclXKjzsYY3HeH4fOwuXJTs4aITTdoVBSCiE/XL8+lOS8+r4U8klp/iw/GYT5Q1thg3QWvaE56dXdpXTtAkPYvd0mmmrzeThPj/muc7FZ81porNbsIjwkLafCC83vJbs50z2IMKDtYmSp2c5ttSZX39fxK6EIgqaRNjIJuPmDoImjcYl9AIxaRWDeB5YkIm++3sCWOA4HQe/vUZi315en0LdwYQHJMloLiVXA3EZNQHfTdHE5vTNrWP7DwgQFqxNL7m63hcftwXM2yUz9BZa6/N4diacUG9npniEs/NhBVUt8t/q4M3jMxwOnMq3s/Zzt6iuS7CxO6TYOpJQW8JUZXLl5cuXSEJGCY+QSVbxGpAJA7F/+itXaXeo966KbSKTLJK/SAQCWWRdvhuqZ2t/JxB7TMaEYjNKwnEpVCCfJRxWvGdFQJHcFWJTyQRVr/BbSwOdTbfZ7jqKqXMj2HA1+x3v5f7Oqd/9vRAYUHgQ92AhvRjdfUUH8XSQRTK6yyykzPJ9upeFtoZSipNiuLhrBYvd3fAN+gP/Rb54O3bneHjTQAcWzOUZZNw+wYbdsTwvaqDBNni3tmJtzyVq6WRmzwtnXVRGH/W/k1Y5x8tbRB+JJMTHgQm//MgEj3AiY/OoeriT8PmeLFq+ipVhIYTO98J15RluZdXwJvk8Z1d7MurHcbjOGsEYj/Xsvfma4r6qqNVMTcFzEs5uZ/3q1Wzcvp+9B49y9MgBDu7dyvrwcNau28TWXfs4cPgoR492L0f2s3fHVrZsWM/6DRtZv2k7u/af4OrjbHJq7Y1Ym/Dgw7jZW7hhhgbjZws1qbFcXeXcR3iQOyTu+Q3k3dxKRGAwy9af436ZgNZG6sF5LPDwZ9GOW/T0FfbCw5k8w91NHpx1BSmkPLxN3IOnvEjPo7CkhMKsJJ7GnePMkYPsuVFISU8OiQGqWtgTpo/wkF1ZS1VhBimPbnPr+gXO7N9A+JKF+Af+wartRzl58RwnF4/g959+Y4LbHFx9fHGb4c+mZw2Utthj9PYkfYWHovoOWtMuErXWn4mO4VyoaeHGdk8Cl65h1ekXVNXVUldXwa1IFxYtXT+g8ECHCUt5Mo/vxHLt4kmOH9jF1o072H/mEhcudZVPOr9/PRt9J/CPb5wJ2H6Yw8b3/ZS9vBrLjbvPyWzoxGyXcOhtK7rXLM00lTzh5uppTJ86n5CjD0gWbnSHr2THbmWNjwsz3Vey7sprqjqsdLQ+4fKG+fjNDmT5lcp3S8y+c5K3X4gHhCQjktABydcgiYMG+s+LZ4KU+pSKD9IxSp6IfmP53h5+yGsygJdkjtJBSryzxAv+GXfAwU4suRpEQJGOUnI1SCcq3/V9yXVJG6VSjQgiYiCIaCEd+//mS0Q+SQQoHXnX0kZN0jnOhrkzetxCdiTVUdTYbvd713amJFs5zXUcSDfTInlweo4hpTV7D5X6zfHQ2YmlvRVzR5+Ehh+Z48HaUk5d2hUObIxk/bowAj2mM2X0SH6etoCwNeuIiIw0XDbFbTMyIpyIP+YyfdQwxjq44R60logNW9l8KY38qn4E4X5vioX652c4+6eEBysdzZUUnvHDa+IoHBbuYO/DwWLtrbSXPyb+SADTfhzGqOCr3MuqMURmq+kNVcn7CBjzHaOmhxjlNN+8kyPAvjGdVN/aSITXtCEJDx1lL0iK2oi/pxfePvNZGBSEj+sUpk2exsQVV0h600izlMEU1+rkWE5s2MTBOznk1YqoLC9pbxVFJ32Y67SYZQfvvk1MbPzcQm1FGW8KS6mpLyd1twtubp9TePgPP0wNIGTjHo6fOMGJnuUoEfMnM23CRCbMDWHPiRO9fj+0fS1rPMfw3fiwTy48WJueELNjEe4TZ+G0PYUyo2pE73tGZym3N3vzp4QHax1vkqPY7fIjPwyby+qoF6TWv68TEa8FM4XRkax1n8joGUGsi683PO2MMM70aK6tnckv//kdl20PeVzQNLC7u9WMuS6dC/4jmeIwVOGhgzeJFzi5ypvpi3Zx8f5jEpOSDE828WbrWp4Yoo2n43SmLtxJ9PMknvX8Jtu84FniA86vncv8wE3sik4Z2C37L+HxYKG5OImnGxwY+eMUFh+JHzzUIv8km30dmOS4mMUHEykuK6O0rMzoh6Qvsl/KaxppMnf8OeFhqi8rbpupbul65luqXpB89o93hAfj721poyk7mmNhCwkO2c6ehxW0i5dG8i7WSMLsZQe5kPd+DprePObmnqXMnzETx9AYnpY2Y1SBtbTQUPiYO1vccXJcQNiRezx50/yO8Nr1T5KqOS1UPjnABh8Hpjr5E3j4hZEb4B3ZY1DhQXLPV2PKO8vqGSNxWrCDPbcL3/VmNk4s522mLGE7a7ydcVu4kZ0Pu8RhK500FcYTuzsYX8dpTA4+yc3sOmrb2iiMP8HeRQ78x/MUT0rqe1XlkEkfqaYlXqIivok3p9gXMiEigoCMEySxpHijizem5I+SBOAf+xL+SMilHEvCS2ViRxJrf+zLdp0yOSAeZeLVIBMyski+CBkvShiqeOyMHj3aEFhs5cvlnNbWStpyDrJ08ghmBhzk0KOqdyd0P/bidL+/DAIDCg/iKiOziTJ4cHBwMOL4vvnmGyNeW2Y+xRtCXMSFTJ92Rs9MdeZdYrb4MWOCA26BWzkc94jrF3bbldPsEh6a8h+RcCCISWM9WHP2iZHkxkgaPYjwYG0tp/D5ZY6H++E6eRJO7tMZP2IkTt7r2P6oEaspn8dH/2DJnAmMHD6OCc4h7E2qp8rUSWf5PW7sms/Ef33D/3w3gXn7EkgsbKKjvY22lmaaO+maYbY2UPQimlOhXkbMuMSN9yxz5+AxRxJLDmP4qAlMcHRlrv3vtnV3N+bOmsjo4ePw3RzN9V6Bkx8jPHTxztKQxqPrl4i6/IDkii7h4cUeT+Z5LDbKnHXF38o0cwY3tvmz2GsxAT3CQwuFjy5yflsoy8PWEbllN/tPRHE1Lp5Hz16QkpZOalETja1vKyv0W07T/i/QR3jIelNA9uMrnN2+iuWLfJjj4orn4rVsOSmVMh7z7OZhtvmM5vcfx+OzYQ9bD0up1ZnM2vqUpNJmO5fwtyfpER7GzSEsLoc7jzPJjN3NoVA/xjlvJLbJxK0dIjyEdwkP9bXU11cQN5jw8PYUWJpyeJ1wlWN7znG3pL0727OF5pz7JGyczb+/82Zt1B3upmYYIQsSttCzZOZTVF43QAdndxJrB+bq16TGbGHhqJ+ZvvggpxMLjdJvtq0s9SncOxzOykVLCdz3iDdtFtqaH3BurS/z3Vew9m4zHXb6jPx/pfOSjk3KPkpn1EuFBqPDk45CSmpKJRtJHtT3Jd4REpco2ZAlTEHES+lwpDMVDwJJWPmx7ntyHDnn9u3bjTAw8aSQvBESEiEdpTyvPuYl6rxcp4SV9HUvlN9ErZc2i/gqnalkZe77ko5TOlj5XTI2i+EgMwZyTSKMiEjTYxzIDEl7C02NzbSY23lnTN/34H/mc0cNr69uIdJ7JqPdd3GnqpX+xiJDq2phfyG9PR62X3lKUWkumU8fk1TaJ479g4QHK6315ZQX5ZKTmUFmyjMeJ8QT/yCGI2v98Jo2ieHzdhN96y73HzwwxB0ReB7cu8n9y6vxcxiDk0cIq47fIz7hIQnPUkjLyCSnsJSy2sGShH0K4UGM1iba0g6zzmMC46f6sWjXPbLq2rFPt2KPpLWtluL4I+wPnMYvw2fifyaTjMruLPeWJporHnF60TgmjXLEY/VxrmRU0ST6sf1BetY/THiwVKXx+s4J1kfs4uDZWO48e8LlvSGs9HLFceMTypu7YpOtLQVk3d3PGtdJTFm4jzOPCyhrFpN+EOGh57ostDeVk/bZhId2qnMTiYl0x2vBUlauWst6WzlX4z2SQNcxjBslVSMWELZ+Q6/fI8JDCQmYz5yAg0SnvOk35rqnKR+60llohHEGO05h5OJLpNWY+vRPn8jjgU4ai54Rv9mJcT8OZ1boKc4nVdD4zsirqwFWSxutlUlcifDEw8GRGYEHuVH+VvC2VL/i5YVQ3H77nl+cI9h3I42C+tb+B34fJTxYqC9I5tnNixyNTuFNc1vvWHyD4a28OBRAsLs7s8OvkSepJXoR34qlvYXiR+e5fO0u8WmlA8+K/yWEB8m19Zq8U744/DoC17WXuJLR1D+mcpskuWT9fY4vc2HmVB+8t97jTat1wGdJDzU/RzlN+1CLnhNJ+dB6cu5f4vrl61xPqTKEh7YnWwlzd8dr5REuFw3QL1stdDYV8yoqktW+Lkydu5rdibXUmKVWTdfLYq6iMeMsGzynMMdvLRsvPCer2tzPQLSDztYynu+ai+e0qbgsP8j51Kae4/RcrqwMQXgQQa6j5TVXl09hlusyQo4nUtJPMyxSredNIlGhTsye6cviHbE8rbE3rBopfXGZs+HzcZwTydlXlZSZWsi9e5jt8xz4KSCatPKmXrwXu0FyLEglCBEZ+to1tbW1ho0lZc3FLpFQCVu1rV7tHMIHmSwRu0U8EcSWErFDkkqKp6Z9wskhHKpnE7F3pIS65HWQd5mIsn+JHSdJwMUbwuZJKvam7dVZX0hNbDCuo8bhteEq13PfDS+0bfsx7/K8aKkpJjf9FSmvUsnIK6W83jykRLwfcz7dp38EBhQeZHMhvRjNEmsks4uS3E3ciGVQISX0ZKDxaUUHedg2kP/wDIcC3Zi+9DRxWTXUtvYtpynCgwyMq6lMv8oet1+Z6LKaHddTyG/pxDqI8GCpesSdQ8txGz8Nx4B9XEg8y+YgL/x917E9sYvolqbX3N2/hIXOzkxZcID7VR00tbdjrnrIzX2LmfndL/zPuLWcTy2nssOCubqEkrQXPCtv607q10ZTlSRIvGckxJOEc13LLW7FRhF9Kpz5E4czwTmQoK3niev53bbdHe7ERnHr8EJmjhyHd+QVruXYP/1swoMXY5wjuFTRwJu6BhoaailIvMjZlU79eDz0RwJ5zLeSuG02Ph4BBO2Lp8c7uD/hwdpE+evH3D+/lx1rglnkPo3Jo0cwcvRkprr5syTyAMdjnvGqoIpaU7vRqX6o8JCZHsO5yIV4THXGNXATey49ITm/jOLsp8SfiuSPOeMZPWUiv337M25hJ9l3PsYon/TjhBCOPs6n2PS287K12BAe9gXjPMKBgJ3b8PXbwraQBYQG+zDeaz+PTSbu7vRi8UJ//CJOcvP2LW7fvsGeAAfmBkQO7PFgO4ERzpHA7QN/4DbZn+0pZsoNL5hu4WGDE//+52gcPf1YENyVndgWY2y8/7GTA5efIhMEto7X7tDdqxY6TGVk3zvKTq8xfPfzbMKvppP2TsF2C015L8l49pgHec2GoWKpuM6xP3zx9V7LzuftvYQH6QwkXlDiBiWeUBRwqTtt/5LZw3HjxhlJgiTDsogUtpc8A0SokFAEeTZIFQjpTGzPBgnZEFc78Zr4GBc+2UcSW0reGXE/lGNJOId0bHK98r8SYcN2Ptt1DeVdZtZEXJWOXjwa7EMjRCyQTtgmPHh6ehohCPbHFexEjJAqQHIce3FFOm+Jc5TKIPK9zCSKsVJf8JzE+KckZZZQabL/T9sf+c+uW2gtf8DFyHm4TXNn1pZHlLd29Gvk/inhwWM16w+f5saFbYR5+bLmbi0F9XbG1wcJD52UPY0ian8kq1etMrgkfFoVKuV0JzJ++DB+murH8j9CCbP/PWwlYUtn4zDsJ0aNd8bFP+ztvqtWEb7zLOfi85Dcv+/7b/15j4euwQIdedzaMg/vcb8zzmUFG27lUVLbTEtrG+02T5L2VlpbmqjLvc25yHnMGj2e0e7biC5sorZndGWl01RDyfUQAmaMYPh4T3w3XCGxuJ66JhNmCa2T4xleLu20t7VQEhNBuMfQQi3eYZjlbVULe+FBeGsuTyXtdAAzh4/Fc/1VYjLqjHLS7/V46DnBZxYeLO2YakvJS4onIf6BUXJOys69Xe5waMVMXKfPYJr/Di7fv8+9Xr/f5/6DeB48TiOvoglDU+m59j+70krB3QPs8p3Mj5PWElVQT3UvMeBTCQ9gbSmh9sUegif/yvAxngTsiOZxcQNNptauEqfCFZkgMTfTWF1IxqWV+M8YzdiZy1h5KsWINe/551obqUiP4aTfKEZ8N4ypQfs4cj+LMkMw7S6Z2tHZ5RnV1khDRRKnPyjUYjBcu+yS9wsPgx3D7ve/hPDQdY8aknfiP/4XxrmtY9PlFMpN8lwQzzK5P12zw+Y2ccuXcpplPDoQhP80ByZ5buRsVj2VzW20Gdt30tnRTluryQi1MrV1dPXrf0J48JjsSfDVSvLL6w0vvrq8ByQcXcrkfoUHO3xtqx2ttCVEssLNHY/QE0SX9TDKtoXhmdlprqM8cT+Rvo5McQog6OBTo6xk795Q+ss6si6sYMmsSUzzXEXktUyqzZ10WO2e5FKNxJTDva2++AduYuulV71KVL498RCFB0G+o4XCC0tZviiEUAlJ7jP+tXa20ms3i5IAACAASURBVFTyipcn/Jj1+wgcgw9z4nF5LxFBzmttKaUy9wmXb2RS1thGu6WG9NhdRHpOZXR4Ajl9PPLEnhA7Q0I8ZRKnv5dMdIhdIXaY2Fofk7xaPBPEthGPQVsiSUk4KccTj02xi8Tu+lCbSvJGiHDyj3/8w7AR+won0h6ZfBJPDfFalfFkcXFxdzM7MZWl8XLzFMaNcGfFqYckDRju2B8yg3xnbae5PJ1XMXtZFzgfv3mLWLL+OGfjsyjrKcsyyDH050+CwHuFBzmDkEQGDkJ2GYyI4iZKmfxBPpSUQ7tiC2311VTlZZNV02K4elos9WTePsB6d/tQC/lXW+hoKqEmcRvB0ycwK3AvBx+U0DaI8ICliariHF4+SSavVpI23eVEiHdv4aG1mYa8W1zZEcBsBxfmHs0lryiFhGMhBE79iX/96xf+Z3wkURnlVHW2U/riKufCPXGIeEhGeTPSbVgl+WDb29JHgpm5sZzSlOucChrPuJEz8d8Zw72CZszd7kiyjW0x1xTQeP8PPCdMZ9G2GG69sX8sdwsPQdP54dcpuC9bwZIVK7qUy/lzmDN5bK/kkgNjLx1DCw82OuPptYRlR54aiXCM7fsTHiQBZkcbrSapYFFPXW015fmveHrzDEc3Lydw7jTG/fYLv89cw964dN50fESoRXkNlcWFFOQWUFpdSU1hEg/ObWbVPBecnX2YH3GI+y8OEDRlDJ7Lj3P0QTp58TuZP/xHnJeeJOrFuzMflvIH3Nq7kIn//Df/+fEXxnhvZmWAN4Hz5zJl5VWK20083jcff6cR/PLbSMP1TNzPhv/yI6N9NrI56la/OR7scbVUvk94mMm//zmWmfMDCPzDFsNq975mP0ejXxjZ3u26VPvDG0Jbzq19bPebwu8jp+C8OZ5nxY2Y+uvbO2Qg0kZr91Rre9oRdiz2Za7/Ts4WdfaaaReRQEpWikotHYaIjCIi2L9EfRc8xMVPKlVIPWjbSzox+SwlL2U76UjsOxxJVHn27FljMCjbfshLnjEiikiHKB4FtkSScnxR16Oioox8CiKO9ngVfMAJRGyQRGUSTiFJkKSChc3bQzweZPBiEx7EGJDO2v4ln2XGQMLSRKS17SvbyPNSEk2KkCPCCZY66opvsc99NA4jhjMlYDf775f1KwbYn+PD12UwU0nS8WUETZ+Ig8cadr1oNPJ69HesPyM8OM6YZeDmOs2B8W5rOZnaSKV92NmQhQdhfQfFdw9xaJ0/vj4+hkupuJV6z57ChOE/8v0Pwxjp6IGn99vf5Hcfb0985kxi1A/f8fPwCUx08e7ZV36fF7KXvTEZSNGwAf9bfBqPhy58O2jOieF0uAeOw37mp3FuLNhwgrO3n5GcmUtefjZZLx9w6+xWVvlMZeLI0UxwD2XrvRJjxq8rh073nbK209mcS/y+ABZOGcYPP49lgtty1h26wvWHyaRk5VFQmE9BdjqpT+9yZdN8vB3GDSnU4h0uDCg8yFihheayJGLCpuM6dxXrL74wEmD+FYQHS302WfePEhEcQFBAgPE8kBwrXctiAgL8cXcYxohffuWXcbOYHxDA4p7fbdvJ+zK2XU0hubSXMvAOTB/2hRVTzk1ubvZi+E9O/HGjhOxe2eQ+nfCAtZ32pmJSTgXiN3UEw0ZMxWlhJLuj7vM4JZuc/DxyM56TGHucQ+FeOI/6gWHjfAneE8ezSlt5bVvrLHQ2l1Lx4jhrnEcw+udfGTHNmwVrD3Lm5mOep+WQm19AQV4Wr1895MG1/YQ6/cTIcUMNtbCdZ6B3+acO5vEw0L79fP8XER6wmGmrzyA2zBHnMaOZODeEyPMPSSkopaQol6yk+9y+84CbTwqM8GBx2W9Mv8jx0NmM/3U0YzzXsfdqIs+ziygWG+l1Es/vX+X0ngNcfpxNVr31T4RaLMT5pxGM81hG4NIuW3JFkA++LpP45fsprLDL8dAPwsZX1g4z5jurWeLihfeas9zpxXXZRLykyqh8dpA1bmOZPN3fKHf/srqt/35Qqto1F/L0RCgrXB0Y57yUdXHFlDZ3TWp1XYeEDbXTWldAUXElZdWt/XhFdF/xUDwejMvspK00jey8N+RXmft4mXTS/OYpj44tZfaI7xk2YzU7Y9Mo6JvIsvs4lo42TOYOOq1WrB05PDm/lqVOM3E5kEtRXVuvPklsf5kEkVDVgYQHmRwRT1kJjZBqS2JffchLxnSSt0smS8RuEXtNvhObSiZI9u3bZ+Rf+JjkleKVKl6f7xMe5FrFrpKQWbGpejw2rI1UZd3ksOu3jHSO4ODdLKrshzwf0sh+trU2ZZB4biNLnWfhuf44B/dtIHSeG/OCNrD1/rv5k/o5hH71iRAYVHj4ROf5oMNYOzvpbGtDwlkNQ9E6gPAgR7W00tmYT+K5g5yPTeRxQTOWwYQHJDFSKy3NJqO2stV8j1Mh3vi5BbLyxF1undnF5rCtnIi9z+3H8dw+dZjTcTc5uSWIRS4zmOk0A495cxj5vQNBRxJ5WpjDq9jdRHo447w/m5x3HnwW2uqKyHkRx5VD61m9cA6O0zxYtusat9PLqe03btdKR10h1Rd9mTlurlGa7Yl0Kj0vm/DgyPc/TcTFP4CF3cbUAg9nZk0YPTThwWqG1nTOBE3BxSuM8HOpxsygcRpDeFiE/1w/Fhx+SWVNrSE8ifhkv9RUlvIm7zVpSY9JuB3NtTP72X3wKrefZlJcU0t1YRpvTnkzfYwvy0/Ek9Q39L1PqEV2VSN1Jem8vH2WEztWsdTHldkuc/BZsokd5+7z/HUGtem7CJo6Dq/QM5x9WU9zaRKPdrjjNl2M/Chi0uvs3KcsNKVe4GyYM8N+HMukgL2ciL3I3jBv5rkvwPdgGk2dndQWppL24pExoy6z6rYlMSWPvMJUChO34/nNLwQdTiaxlwjUdVPeKzx0h1pEXL7H/YwsI2xAHtI9S04Rbyp6x/vZbrUkcmqvTiX+ZAShno5Mc5yDW/hZ7uZKTO673h22/bqvyshAnnlmGUvcffEOP09iszGX0rOZDJAlP4F0FlL6UkpUds3Qd20iYoF4K4gwIRmVxR3P5h5nS1AkHaUo9RJmIXkRbKUaJaZPOiKpDLFt27YP8ngQ0UHCvcSdUI4rKryIATbBUzpKceWTRLhyDhE3xMvA9ntPA9+zcuTIESOUTJIdyUDF3otL2igxjyJKCDZy/fa5LeR6ZEbezc3NuD4Rb2ztlnURNSQPzrlz57rwsjTQ8OYeRz1/Y+x3/+CbKctYceKl4R5s/89+z+UO/lNHMy3lr0g4vopg57E4zApk2cEEsposvcQm+wN9sPBgbaW54hVXAn5j4vAf+fH3mbgv3cmJ+GyKGztoszcWhiw8yBVZMVUXUZTdFfLzKvk5yQlRHFjuitPYSUzyWM2++8m8ePnKCAmSsCBjSUokOW49i6eNY5bvWtZHvej1e0pmAQUV73FpNsD4lMKDhM9XU5Jyi6t7Q1nsMpkJ4ycxZfpMnGe74eY2m9mzZjDNYSIOMzxYsHofJ+6IK3t/oTfSCUqZ5WQeXdzBhoDZTBv7O2PGOzB1xixcZrsZs0ZzZrviMtORKeN+Z8w4J+auOMilvLeu8/b3e8D19wgPXd46jdSk3yP+YQopBbW0iKdTcxWFJ7xx7y/HQ8+J3vV4yDOZaal+Q1FhNjEbPfGd68WcNRd5lV9AfkEBubGbiJznhsPsMPY+e1tOUxLbFhS9obi8HpOlK5zPaqqgPOsRMRfOc/H8eeN5IM+Et8tpNi+awoxJDkz0WsOh8+c51+t32fYC589HcfvlG4rsPXZ62vDxK5ambNJit7BozG9MCY8jPle8RWyvTyg8GJMdbbSUp5J4aTsbAucwc/IExk2ejpPLbNzmuOHm6sxMxyk4TJ7GTN/VbDnzgCd5NTT1p7VIacSmEnIenefI2oX4OE1i7JjxTJzmhLOLG25z5jDHbTaus5yYMXUCo0eMZPycdWy/9rJf13Rbi4f2/pUKD1iwdjRTnX6dMxEL8HFyYLzDDFzmzGWuuxuznWfgvGAdq8+86ikZa2kpJz/xIifW+eEycRwOjvIcccfdfQ5zXGfhPGMGji5L2Xb9FWky0P9oj4eFzPxxGKNd/fFb1C3ILXBnrtMEfhqS8GDB0lZHzrH5zJvhzYLNV3lqb+tZmqnJTeTesXUsnzOJidMWsepQHA/zao0Q5QF5YW2nqSSJB8fXsnK2A6OdFrHyQBwPc6ppsOetCG/ihfOe+EXxVGi5u4Zlri7MXnGYS8X2nVXvK7C2m2htk/7M1jtbob2W4hdXOLNxEZ7TJzPCIYiNl5JIKW+mtZ8JoD5HxJwfQ9QGP1wc5xNyt5GKPjnJxMtA7C/x5JRE1f1N1Ii9JhMuUmlMPAc+JJeU2E8yqSSeo2L7iIBh74Uqwod8JxNIMgElYaOyz1BftmsTm0lsJ5udaL+/tEmSZ0ob5Rlty/FgbcwiO24zHj99j+v6OG5n1RtjQPt9/8x6R+41Lu0MY7bfDmKzS8kvzuXl+Qg2hobgtTORBtHs/swJdN8hI/CXFB7eufr3CQ/Gxl1Je149f8rDpy9JefWcV08vEDnnd6Z5R7DhSlaf5JL2Z+ikPvUQW+bLzNNEpnovIThwMYuDNnP89nOevHpC/LFI1vzhh7uTE+6L1rL52AViow8R4TyS6R5/ELZpPetCAvF2W8rW501UtHTQUpFH9vO7xEVHceHUYfZtXk3Y0sUs9FvAwqBQ1hyMJT67mhpbWECnGUtdJsmPHnBPwi5u3yDm7D52z/+dMY4hRjnNgrfWiuG23ZB4lMNLZjN84iI2Xo/jyk3JRn+Tq8d3sH2pJ2Nn7yImp9LIkN6rxdXZvH7xkDu34oiLucilI2HMm/Q7MwN3sz++7O2AvVt4mDdpPOPmriBi/Xpj5llmnwdcItcRGS7xYisJCVtD5Pr1RK7+g7Veoxn209whCQ+Z2c9IOL+diABfPD288Vq4kjU7DnPs0m3uPn5ByvPb3Nvjxcwxjizcep1bxZ1YWutpzr7GgaC5BEUc4/gT+6RuVloybxB3JJKAsAOcfVxAxqMDbA1wx33eWrY9ruujaNvQstJUlkt+ZgrJj65y/UAAk/41nKDjL3la1onVXGOUx4q7eoWrly9z6fgG1i2ezYTh0/HbeYFj5y9z+fIlzu2LZIOPJJecyeJthzgUJd8PtERz+UoCqeUNNHRaaZPM888ucWLzUhY4OzDD1Y/Fm85yLbmMps7eAkLXVVtoKk4l9VkC9+9LHPwd7l0/wnrfqbh6hbL6TLKRdMnWlco+0tlIjoNZs2YxadIkY6AvM/Qyey+dmsTRe3t7GwkW5V22FS8oGeCLp4O45olgMWrUKGP2WzpE2yLeEaLMS5iWdET2HgE2lAd6lw5QsnbLgF9yLdh3kPb7iOIvg3vxPBBc++us7be3X5eOVbIri9ufdPji8SD7i1eXZJIWVV46UZk5F5Verl8W6VDXrVtntE3EGGmn5L2RRdo+bdo0Q3SQfa9fv24IGlLSrbWxiLTYI+wOmsFM7xCWHn3xyYQHS0sZb17Gcm7rMvwcRzHe0ZeA7Ve5lVn7nucffLjw0Exj6WOOe43E2dkTv7XHOJ+QRampT2JJAfqDhIeuO2Nta6DuTTov7l7mwp5VLPFwYa7fatafe0KeyfqugNLRRGfRCda6TcVn+WFOpPR6UNrf7vesW2ktTSPt9nkOHbtCfJGJ+n4FYTmEFXN1ITl3TnHowCXuZ1dQ2Y8jj6W1jpqCZB5Fn+bYro2sX72SFUuDCV6ylGV/rGbN5j1GboV7L/MpaWjvNfP1zoVa22iuyOX14+tcOr6HHetXsWrFUpYEBRIYGEzw0uWsCF1DxNa97D9+mZiEVHIb+sHqnQPbffFe4aGr3XS00FJZSF5GilEi90lCHFGhU5nqsIgVR+6TYT8Q6Dl0X+HhERmluby+spX1a0Lwk8oFo35npKMPy0JDCZHFz4npY0bw84gpuAaHsjIk1Cj1JuXewtZtZ+PReHKaTJRLouOEGKIvneXE0SMcPXrEMKjFqH67HGSN7ySmjp/AOPeV7DhyhMO9fpdtjxrLqQvXuHb3BUnZFUbSN/vnZE9zPnTF2khlxg0uBE9i1PRwDt7L5o3ZdmQRlhrJfxzNtXOXuHQvw0iQOKABbKml6NU9Ys+c4WhMOhWm9rf9dc91dWKqzCbj8XWiju5mW0QYIcuCWRK8hKXLQwhbt4Xth85x6W4KWeWN3aGhPTv3XpGcNK3VFKcmcPfSMQ5tX8/akOUsCw4iKDCIoOClLFsZxqqILew4eILT1xNJyqsaONdC76O/55Pg8wk9HmRI0ZbD89jzXLh4mztpvUMJ33Mh7/z08eU0bYeyQkctb1LuE3dmLzsiQ1kpz4WgpSwPWcuGQ1eJTio17msXS6y0N5ZRknqX6JO72R4RRujyJcb9XLI8lLCIbew+fp34zHIqhVfWFtqbU7l36ghnribyLK+u2wPAAu25JN28wIXzN7nxsqJnoGWtiydmpz9uox1x3xJDVHR3ZaPLRzi6KRiXKd5sulNCTi9RTkZqlRSmPiPxzi1u3Ywm+tw+IjzH4jBzGWEnEikUIhuVzXJIuXuGw5FBLJzjzFQXf0IO3OoWD2z/BRs+/bxbTdQVPCPhVCQr5k7F0XkegeuPE/U4h5LuRJjv7mXBaiok89kD7sTEEBMby43rUZwMc2HGOGfmrj5DnFHG/t093/mmw0RHdRqPovayZclc5sycyXTvVWw8m0hqRUu/wom1pavU8cN793nwIJ6Ee7Fc2B5MoMccnBbuIrrE8o7gJ5Mp4kUpngAymSPCgtg3YpOInSbeCTExMcZvYq98SFULERBkIkfsNfFqEI+G/mwlsfnErpN8WmJTiY0k9t5QXmKjSWlOmYQSm0kmnmQSS44pbRP7Ki4uzriGZcuWIeEdXXZdO/VZcdzY7MWwYV5svpVLdv1gk2pDuaK323TkXObC1pXMmreHO5XtNLTUkHt1PRtDluO+9ZFRXWRQ7ejt4XTtTyDwtxEesu4cZKPHJP6z6CqvSrpzPPQ03ArmTJ5eO8b+yHWsWxdORMg8XCZOYNayQxx9WNrzgO3ZxViRh3QHZTc3sSHQHSdnD7wXh7BmxwnO30oio7iY7BfRnF/ljsMkJ+YGbebgzRSyqlswVWeTemYlwXNm4DRtMlNdFzIv/BJJzRgzMXXpccTuDyVgnhee7i7MmjUbD7/lhG47ycUHaUZd4jZ7lrc3YCm9ycmNqwhdEkxw4CIW+brjNGU6PhGXiUur6JN40EpzajRXd65jUegpHkuZauP5bcVUnsXr22fZcfgeqZWN3d+/bXl73k2uHljPyqDF+Pt5MWfaBBym+/DHkbskFHcnNpPNO15zc4c/vmOH8et4J2a7yWzdRywuM3Gd+DPff+/OipMJ/Xo8PIlaw8Jx03A5mE9muswUrmGF/3JCtp3m0sNsCmtryEu6zY3jW9kYvpxA10k4uoWxPfolWUbHI1UdmsiLO0V03CPi85p6udtZ6vIoynpFfEolrdYOSuI2EBEURMCGKBJq+pY4k8YLmJ2UJV4g6tAW1oUGs9THiVFjvNh4I5vXdRYsUss64SChixfiv2ABC3zn4j5rBlMmOTLLyw/f+QuMDmK+91zmzpRymo64ePrg49f1vXQs7y6BLPDfzqW0UkrbLJjKXpJ8dgUu4yfi5B7EqoM3uZ/1voFkJ1WJpzm1LZTg4CUEBwUQ4D2D6Y5z8N9yiZiM+l642FghA21RnyVpo7jfiaIuuRMkllDUcVGnpWKDDOxtAoAID5KcSBIdycx+T231fuqtizeBDNyl8xnqSwb+IlbIfuIK+L6XdMhSTUM6S+nkhur1IOEZ0iZb/WqpmiHJj+Q7CZMQEUFcHwUHW7ZnMQBkpl3u3fvaLL+JkCMdt01wkaRu7Q35PNoXzKqI3WyJzX+vKPC+Nvf9rbM2lecXI/GbOIoxUz0J2HaNW+kVNL0fOtpTj7NvmS9uCzZzLKuPx0Lfk8hnawst1anEbAxh9/Eb3HtdQeNAt7VbeFg/ZxKegfs49HRwA8bSVEDOw9NsX+KDx0wn3PxWsfncQ5JKTf0PzruFh3Vu0/BdceQjhYf+GvqJvpNqI+ZaKotzyEpPJSU1g9d5xZQ1SKz2EIzuXpchIXyttBieIemkp7zi5csUUtKzyC4oo7qpjV79Sq99B/lgqSL54nrCfd2YsfkJFd3JJfvu1fnmEQ8u7CFiteTfWMZCpzE4zFnDtqsDzXR3CQ/pe2fj7r6SNacfkVaQyotDgfh5zsFt9mzj+SLPmKEsbt5LWBQZTUp9E6/jz3J+V7jx3JJn17tLKGFhIQR6OeMycyZOcxeyPCyM0H637dp/1eZjHLuZboQcDvLX6QvNAJ+ttNXkkHc9HM/x0wnaG8ed/IEy9A9wiI/6uiv5YnNVEXmvU0lLSSM9M5/CsjpjwPOhzMPSTltzDRX5WbxOfcUr8TwSLucWUVzZhLnzbULpj7rcXjvJ1X1K4aHXwf/UB0t1FpUXFzF5hDNLTzwksfrjWWJpa6KpspCc9BRSUtLJzC+nsnGgZHdd//3GigLyM9NIS+2+nxVd2H/w/bRDwdqYzMNzW1i7JILdT9uo7xbGJG9IaUocJ/ee5FZmXe8wOrEk2rN5dH4321YEErBoPvPcpuEweToeq05x4WlpV3UeSzvmvBhOhM/DY8ZMZi1Yw8Zzj0mtbKFloH7D7tp6Vq1tht2dHr2DiAXOzJodzKpDd3nVSwzp2drIJWFtSOT6vrX8Mc8H33nz8JvnjtOksUxyWkL48Xgy7D0y7Hbtu2ptq6O1KJY9i5yZ4+SKx9Jt7L3+ipJWBpi0AkvtK55F7yMiOIjg4KUsDfRlrrMTLj5hhJ9+QblFggt7v8RuEa9N8ZyUCRHxOhAPUhmsyyLfi3enhJ2Kx+WHJIGU0FSxpcR7VDzHbHZc7yvo+iSChNhU4ukq+fzEPhzqy5aDQiZtRFyQySMpbSz2nNiYInzIpI0kBrfZaVZzCWnXd7He04mxgVEkSG6aj/9b9XuplupnxJ/aSJBXIKtP3eTa1dPsXxPIspCNRF7PN7zQ/sx/qN+T6pf9IvA3ER5kRuAcB5Z7MzX8Dq/L+7rNioL82sjkv8J1mjHb6OjohItvKDuuJpFa/b6nm4Xm9FvcvRlHTGI2xb18tzqoy3nCo1PbWHPsIa/KuqpWGEha26GjjJToA+wOD2HN9tNEvbQpy9Cad5/7p7eyMnQjW49c4Or9VDLLGvpVRruOZ4LWl9zYt4ENy4NZIjF24VvZevQOyaWN/Wan7qh7Q/HrVJ4l5xuxUEO1YeUPeO/sbjasWMbSpctZHraJvVeSSClp7F0ztyOX+GNrCA9ewYpjj8nKzSM3L89QMEXFHOqSk/qU9NNL8J2zkk2Xn5HW2oeLHdm8urmLcA8/As+9oaC2kbqqKqoqau1KDZnJubGHHYumMWGqC26L1rDzeioZFabeopLE0cnS5xS9P1poyLjC5UuxXHqQz8DFiDqpuL+PPaHzme06hzneQSzeFMuT4nqkrLm1uZya9JucPLCX/Xv3GFVepNLLn1r2HmTPgas8LqylRvJjNBVSlnSODRHHiU0uoUzKpgzyMqdd5tyOEBb4+uDt7YO33xLCD94mIavqHRHK/lCCmwySJW7Qy8vLqBktdaMl6aStvrO9ACDrUtVGxIHBFuk0xUvA1tHYn3egdRnoS+iD/TkH2la+l05Y4gWHur3tWCIkiEovooW7u7shooiQIt4dMjMg4TAiZthe0mEL92U24n3tlnAL8cR4m9m5k46WcooTDrNh4QLC913jVtHHzM7brqT3u9VcSGbCObYuXcX2a2lkV0soWe9t+vvUkXOdizsiCF1/kujCTtoH7fDFAO7E1NRMu5TR7O+gtu86m7FUXGFPwFyCw09yJvktjrZN+r5bW/LJjD/NxsCVRByM5n5mJdXvS/7U0UxnSRQ7F/uwfP05ol73Nef6nkE/94uApZb02H3sWBmE38GXVLd0VbXou21neTxxR9cS5OGO+1xP5nr5E3b4Hgk5db2fxT07doVkZJ8KJFjC5a4mU9jSQlN5Pnk52W/DzexDz963nlNA3ptaWiS30qs73Ll4xDBuxcD9FMvhM7HEPinoesb3tOFPrlhbMNckcWHpdOatOMCRewXvfRb/ybN9BbvLg6uNlNNhrPb3Z+GWm8bs+V/hn22py6fmxhp8XBaz4dILXvYqc/43hb69lsqCDFKSUsmq7uwdLjdgk6SqRSVpN49zJHI5y5YuY8nKcCL3X+fB6wpqbF5glg46y+5wbucWtuyK4kZGrZ03x4AHH/AHEe8bsmK4eDKKS3dSKRuQFDKyz+HRuZ1sXuJveBEs8g8gYOkatp57yJP8hgGeV/2c2mqm05TFzf07OHUpnsT8ertwqX62l69ac0i5dZjNi+bh6+2Fl/d8/EN3cjgmmRzx63/PS2wSyWcl3p4ixoodJl6ZUg1CbA5Jrm2bzHjPYXr9JMcUUWCoNpXYapK0W0SH/jwjeh28zwfZVyZnxH4Szwe5fllk/fDhw4anhr0taPQp+0Pwc1vCxsQmqvqEoPQ5/Ed+bKU68w4xm32YOvI3Rg4bxthZywg/8ZD0T5nE8iOv7v/Sbn8P4cHIyWCiub6W6obWAWK4OmhtbqChptpIOlddXUNNXQNN5vZBDWlru5lWsxnJJNzZx4qWxDBtLU00trTR3mk/oJUHRyft5maaGhpoaGrBZGexWzu6spY3NDTS1GzC1NpOR2d/M+s2uon3RTvm5kYa6+uNB0RDQxNNLa19zmvbXqIturMat3e83/i328VYlVJa0qaGrvPUNzTSbGrv5zwdtLY00FDfQENLW4+buc3dfMjv7W20t0giygYaJYPzO8/cDtrNTTTU1lEvJUstVmPwKAPIt5ta6RCs6+T+VnB06gAAIABJREFU1lBbJzWru665b/OG8tnSbsJkMmNqlTSgA78src00N9RRY+S3qKe+0Uyb7T5aO7G0m42M0jLo/XSLyTiHQUVrJ53tJhobWzC3904KOdBVS2yiqamBurruXBx19TQ0t9LaYY9n/3tLByMueZLDQwb+skjnI23rTyEXDojXwGCLzdWu/7P2/61x/+2zV/e/Wc+30pF9iEeFbUfZT9pma7fMDMgiGEi7pY32naSsy3fSpsHaLS6KPUJIRzk1eVGsc/MibNd1bqdXGuEytuv40++SiKvVRFN913Ovw2L/vHrP0TvMmJoaaRCOvbeqSu9jCA7v++90bS3Gadezu76hhZZ3//y9DyqfrB10tLbQWN9AY7PZ4G2vhIvv7CEuv9LuWuobTZgGNETf2VG/6IWApbs/q6dOvB0G+u91tmJuaaS+J9dPXc/zpdfh7D9IIugW6W+6nttGojWjGkdX6NKQ+xJbVRDjGWylU6oAmFo+4bO3meaWLltgcG7bN3CwdStGJvyKQoreVFBZb3pntnOwI/zf+92KqaaU0uJiisq7JkU+7T35SEQtHVjMjdTV1HXZM31sxo886pfdTbyyOtuNflBKbQ8dZ3lmtNDcaLMlbc/szq6y8rZWdbZiapJE7ibM9rW8bb9/0LtUnTBjaumyq98/FdNBm6mJpvo6w46pq+t+BpnE2+xDblyXd7S5uYkWqRIzBFtK+jFjfFBXR53xrKyjrqGJZnPvqmL9Nd3eJpG8CWKHiU0itpjYHGLn2Nsk/R2j73ey/YfaVLL9h+5jO6/YVGI/2duS9jaVbTt5l1DpuooSCgvLqWn7wBBB+wO9d11404q5oYrKNznkiKdWWS31Mrb7ECq89xz641AQ+JsID0Npim6jCCgCisBfGAGLidbGQlITk8ksqKam5f2i11+4JXppioAi8BEIGJWujBKog4vAH3H4r24Xq02cson9X10LtUGKgCIgHpSdHR1GctDPrwGIx6aIbF2TnIr+/z4CKjz872OuZ1QEFIH/kwjIrIl0djDQZPL/SVi00YqAIqAIKAKKgCKgCCgCXz0CKjx89bdYG6gIKAKKgCKgCCgCioAioAgoAoqAIqAIfDkEVHj4ctjrmRUBRUARUAQUAUVAEVAEFAFFQBFQBBSBrx4BFR6++lusDVQEFAFFQBFQBBQBRUARUAQUAUVAEVAEvhwCKjx8Oez1zIqAIqAIKAKKgCKgCCgCioAioAgoAorAV4+ACg9f/S3WBioCioAioAgoAoqAIqAIKAKKgCKgCCgCXw4BFR6+HPZ6ZkVAEVAEFAFFQBFQBBQBRUARUAQUAUXgq0dAhYev/hZrAxUBRUARUAQUAUVAEVAEFAFFQBFQBBSBL4eACg9fDns9syKgCCgCioAioAgoAoqAIqAIKAKKgCLw1SOgwsNXf4u1gYqAIqAIKAKKgCKgCCgCioAioAgoAorAl0NAhYcvh72eWRFQBBQBRUARUAQUAUVAEVAEFAFFQBH46hFQ4eGrv8XaQEVAEVAEFAFFQBFQBBQBRUARUAQUAUXgyyGgwsOXw17PrAgoAoqAIqAIKAKKgCKgCCgCioAioAh89Qio8PDV32JtoCKgCCgCioAioAgoAoqAIqAIKAKKgCLw5RBQ4eHLYa9nVgQUAUVAEVAEFAFFQBFQBBQBRUARUAS+egRUePjqb7E2UBFQBBQBRUARUAQUAUVAEVAEFAFFQBH4cgio8PDlsNczKwKKgCKgCCgCioAioAgoAoqAIqAIKAJfPQIqPHz1t1gbqAgoAoqAIqAIKAKKgCKgCCgCioAioAh8OQRUePhy2OuZFQFFQBFQBBQBRUARUAQUAUVAEVAEFIGvHgEVHr76W6wNVAQUAUVAEVAEFAFFQBFQBBQBRUARUAS+HAIqPHw57PXMioAioAgoAoqAIqAIKAKKgCKgCCgCisBXj4AKD1/9LdYGKgKKgCKgCCgCioAioAgoAoqAIqAIKAJfDgEVHr4c9npmRUARUAQUAUVAEVAEFAFFQBFQBBQBReCrR0CFh6/+FmsDFQFFQBFQBBQBRUARUAQUAUVAEVAEFIEvh4AKD18Oez2zIqAIKAKKgCKgCCgCioAioAgoAoqAIvDVI6DCw1d/i7WBioAioAgoAoqAIqAIKAKKgCKgCCgCisCXQ0CFhy+HvZ5ZEVAEFAFFQBFQBBQBRUARUAQUAUVAEfjqEVDh4au/xdpARUARUAQUAUVAEVAEFAFFQBFQBBQBReDLIaDCw5fDXs+sCCgCioAioAgoAoqAIqAIKAKKgCKgCHz1CKjw8NXfYm2gIqAIKAKKgCKgCCgCioAioAgoAoqAIvDlEFDh4cthr2dWBBQBRUARUAQUAUVAEVAEFAFFQBFQBL56BFR4+OpvsTZQEVAEFAFFQBFQBBQBRUARUAQUAUVAEfhyCPyXxWJFF8VAOaAcUA4oB5QDygHlgHJAOaAcUA4oB5QDyoHPwYH/qm9sQhfFQDmgHFAOKAeUA8oB5YByQDmgHFAOKAeUA8qBz8GB/6qqqUcXxUA5oBxQDigHlAPKAeWAckA5oBxQDigHlAPKgc/Bgf9qaGxCF8VAOaAcUA4oB5QDygHlgHJAOaAcUA4oB5QDyoHPwYH/am1tRRfFQDmgHFAOKAeUA8oB5YByQDmgHFAOKAeUA8qBz8EBFR5UeFHhSTmgHFAOKAeUA8oB5YByQDmgHFAOKAeUA5+NAyo8KLk+G7k+h1Kmx1QFVjmgHFAOKAeUA8oB5YByQDmgHFAO/L04oMKDCg8qPCgHlAPKAeWAckA5oBxQDigHlAPKAeWAcuCzcUCFByXXZyOXqpB/LxVS75feL+WAckA5oBxQDigHlAPKAeWAcuBzcECFBxUeVHhQDigHlAPKAeWAckA5oBxQDigHlAPKAeXAZ+OACg9Krs9Grs+hlOkxVYFVDigHlAPKAeWAckA5oBxQDigHlAN/Lw6o8KDCgwoPygHlgHJAOaAcUA4oB5QDygHlgHJAOaAc+GwcUOFByfXZyKUq5N9LhdT7pfdLOaAcUA4oB5QDygHlgHJAOaAc+BwcUOFBhQcVHpQDygHlgHJAOaAcUA4oB5QDygHlgHJAOfDZOKDCg5Lrs5HrcyhlekxVYJUDygHlgHJAOaAcUA4oB5QDygHlwN+LAyo8qPCgwoNyQDmgHFAOKAeUA8oB5YByQDmgHFAOKAc+GwdUeFByfTZyqQr591Ih9X7p/VIOKAeUA8oB5YByQDmgHFAOKAc+BwdUeFDhQYUH5YByQDmgHFAOKAeUA8oB5YByQDmgHFAOfDYOfFHhob6+nsrKSmpqaj5bAz+HWqPHVBVQOaAcUA4oB5QDygHlgHJAOaAcUA4oB5QDQ+PAFxUeysrKyM3Npbi4WIUHVdeUA8oB5YByQDmgHFAOKAeUA8oB5YByQDnwFXJAhYev8Kaq6jY01U1xUpyUA8oB5YByQDmgHFAOKAeUA8oB5cDn54AKDyo8qKKoHFAOKAeUA8oB5YByQDmgHFAOKAeUA8qBz8YBFR6UXJ+NXKocfn7lUDFWjJUDygHlgHJAOaAcUA4oB5QDyoG/OgdUeFDhQYUH5YByQDmgHFAOKAeUA8oB5YByQDmgHFAOfDYOqPCg5Pps5Pqrq256faoMKweUA8oB5YByQDmgHFAOKAeUA8qBz88BFR5UeFDhQTmgHPgbcsBsaqa5sY7a+mZaTGa9h3/De6hGzuc3chRjxVg5oBxQDigHlAN/DQ6o8KDGqg5YlAPKgb8dB8w0lGTy+s4p9l1NI7e0HtPfrg1/jU5QjRG9D8oB5YByQDmgHFAOKAc+PweGLDyYmmqpra2nrqEZ8ycycMvKysjNzaW4uFgHPp8IU/3TfP4/zZAxbqmjvLKUwopKqkyf8brMzTTWlPEmN5vM3DKqGpppMX/G830wV00011dRUZhNZkYm+aU11DaaBv/PmxuprSimMDePnMJK6kzmwQfX5hbMDWW8ycsht7CU0pqmT/a8eve+m2muraC8KJfsnEJK61po/pz32R73ljdkxp9gh58rs7c+4kVezeDY2O8/yHpzQyVvyvJJLyogs6KWBrN5UBzNpibqqwvJKi4gp7KaquYheGGYm2mor6TwTS6pb8opb2ym+S/F3b/S/0iv5d3/oGKimCgHlAPKAeWAcuDvwoGBhQdzEw01pRTnpJP28jlPH93n/sNkXmWXUveJDMNPIzyYaTU3UJGfwetXSbx48aJ7SSY1r4LKuk8nlPxdbqpe55d6AJlpqHtDdlY8Fx9cZOfVfSw/uYc1N25zs8L0SQeGtntsqskl7UEUp3dvYE1YKCFha9m0P4obz3IpqhvCwG+QAajtPO+8m2opzc7gdWomubUDDUpN1Ba+5FncOU7s2cz61SGEhoawKmI7e8/e5kFaCbX9PUtMjZiqMki8doyDW9exOmwVq9ZuYduxGzzMLKeisb92mWmqLiT/6VVO7YwgcnUYYasj2bj3HFEJ2ZQ1mWn52La+s5+JVlMVeU+iuXxoKxvCwwhdtZa1245yMSGTnPLGQQfp7+D5zjnex2EzjYUPiNu/DJdxswi48Jq00j9/TnNLHTUV6dx9dJEdl3YRfHwjvkc2seDkAdbefsC94krKm/u5LnMDleXpJDw8xZpTG1l0ZAMLTh1g3e373CiqocHUPz/M9YW8fBXL/su78D+8nnmHt7L86lXOpeeS29DfPZZzm2g111CQkkxGZj5FdUMQsD4I237ap/sPLhIqRoqRckA5oBxQDigHlAODcGBg4cFUQ0VBKi/uxnL9ShTnjh/k0PGrxDzOouwTzep9GuGhGXNLCS+vH+fkni1sjFzHmlUhhISuIuLADe6nvaH6E13vnxssqEH7cfjJAKSZitwU0lNSSM0uprS2efBBvKmBxupC0p8+41V6LoXltdS3DDSY+VT3ppny4iRu3tjE3B2BjF01g/9vgSPfbNnJptxP7YUgA7BaChOOsXflPNxnzmK29zz8vGYxY7oHizec4fKLEur7G9gP8lAY/D6ZqM1LIPb4YY4ci+ZhhZn/v733fqoq6f99n7/iW7fO99zvD7fqW+d7zqm69546zzMzTjI7Y45jwgCScxJQQUyoiIo5YcScyCpGQAUkKCBIjpu82Xuz2XFx7/nhdWqBSBDjwBPm+UzVKsC91+rud7+6p/u9Pt1tfy8dK732FooTD7I32A3H1Y6s8/QjKCQAr3UOOHpsZEtcOnmtlhHRGVYshkZqHxxmp58Lrs5uuPv44OexjuXLvdh2PpPsWh3mEekp5g6aXqZyfZcnDiuccPf2w8fDGRcXb9wizpJa3kVHz9hMUu0WA8baB1zd7U+AqzOO7j74+3mwbvlyPLae5/rzGlpHNUfGiDPFSFPWWeI2ujHf6QCp9UY6Lb+XbQVjVzWvck7isd+TGbsDmHUgnGWHN7Jkrw8/REUQ9PAFma3dWIbxo2DWV/Ii9zzBsR5MjA5j8aENLIjxY1bsDlzuZPNSb8E4rA9WzeIeGt8kceBiBPN2+TLzYAQrD/ozded6Vl9NIL6yvc+Uej+6zkavvZ3CW6e4cuk2SQWavv79/e+NkdbDyirP/HTfIBqJRsKAMCAMCAPCgDAwOgMfNh5sejqbKil58YK8ojLyH93kdsI97udV/30ZD4oZxVrL0/gY9oX74bZ6MfMmfcuEnyfy/TQfoq5nU9z9iUG5YsNmMWPq6aGnpweT2YJVDetW7CjKaJOqXnrV0GObBbOp/54ekxmL1Y7Sq2C399/3bjCs2LHbbNjUa9jbP/W7NmxWK9b3PlMrTEFRP7dZsVqH36umbTGraZswmfsnte/SUwfLappWc3/+jEaMxh768mhTUEZM2oY1jr5yWbEMKZfZasOu/rvd3v+zbzCuPsfen3c1/2r+bOrnb0EbKPPbz6w2VZPRIRyW/nsDfXWyqKPwchR7IyOIPHSd5Bc1NGv1GEz99TSs3AP3W5ppLUsk1tODTVEnuJCeT3FDJ7puE5ZPaTDwjC/+aaG9uYiHj+IIT77BiZRdrNztzfRDx4mtG2PjQTHRayrhVsQq3JzXsyEukwpdDzbda9KiXXBzCiTo8AMqzKO/bf645h+pJ8VGr62Toosb2eATSlDsHV6be983ghQjveZnnF+/Fvd1IYTHPeJVqxGrYqIl/zyHg9ewxnkjW9Oa+wyhd3Vo09JZlc5Jx8ksc9vN4dRi6nWddFY+4KL/TBav2cr+xEIaLMPzaNVk8ShuA6tnrcQzroCXGgM6TQ5PzoXjvmARDgfzKGzsHoOoBxvmzhpen1qH43I3gg6l8KReR4+2itJLAbgvWUPAvgTS68dviYdiquBx3CZCnN1YdzSPVrNtTMrVXJNJ/BlH/ltkOEGP83jW3k13Txv1FUls2r2Yv+w9QlRuOa3DTAQLDSXXiI3z5b9v3UV0WTu1RgMNZQkcOhvI/wgPZ3NpJ1VDTR/FgmIp51J8AAv3ROCQmEWR0YzFWM7N6yEsjN7EkuuPKLL0jlIutS/v4c21zUT6+eO1K4G8Lpssz/jivmp4+/nq/kDSlbdbwoAwIAwIA8KAMPCZDHzYeFAn0DYrFrMZs7kHTV4qd5LTefD3Zjz0qgNRG+ZuPfrWXB5e2ErAolms2radNbMW4bfrBrdLuj8a+mzv1lDz6imP05JISLzLvcxXVLZ106XrwqDX0WUeOUhTsJn16GrzyH6QQnJSCqmPcskrb6HbbqarXYvBaOp/M6hO1CxaWhtqqa1tQtNhwDxQOVYD2tZG6qqrqKptoFFr6ZsU9w8CFexWE4aWGupqKqmqbqCpTYdRUctrRd9QRGHmXVJT00l/XobG1Ivl3cTehtWkpb3iBdkPU0m+fYMbt5JIfpBDYaMRo+XDb34ViwFDcxn5D5NJSUgi5V4Wua/raTGYMWtb0HZbMdl66bWbsXS30ajmvbKSyqoa6ps70Jr6tVJMWjqa66mpqqKyqpbqxk70lq+ZIPXXb/vL21yO9sX1t/nMnOvAmrCDnLlfTHlbN9YBPYf+VIx0txaRFRdOyNoFLJi3hOUem9l9/gF5jT10f0SDrx+Eq23GgslkoKvHSGfFRcKPBjLz8NgbD4qpHWvRYcJWrMQ36jK3yrqxqmwoVvSFp4kNdMPZL5YLZWNreCiWLmy114hx/w2ngAMcTG/A9I67Ie1EsdBrqyMn8Sb3H+dR1NKDpe/kBQW7qZbM0+GEurmxZt9TOi22d8aFvbOUmrtRrPhpLiFnn/O03oRNNbgMGrRZOwlYvpqAmNvcqbMN6eRtND2J42SYM3OcD3O/uRutVTUGjTQXJHN942/8tCSGa4UNIybNQ/I7lJ2P/W7X0ll7j6Mrf8Yh5AzxWfV0q0aWrRurNouzQStw99/D3rTaEZEBX5HWB/JhKo7neKgHTp67iCvpwTQmkTx2DB1VvHxxnQP5VbzWGvuXSCg2TPo6ilIDmbEtAvd7z8gdutzCVsvdO7tx2hPI9Fv51BgtWBQFm6mWnJxTuEY68P3VXLJaDO9MBMWqw1JzDs/dXiw+f41Ttca+exTFiq7qGjtOrmdG7CH21lrpGWZyDGioYKq+zfmt7qxZE0JEUj06s2r6DnwuP7++DxPtRDthQBgQBoQBYUAYGB8GPmI8DCSoTmYstBSkcffv0ngYyGcv5uo7JB4Iw3F1MEdy8zkV4oBXYDQxCa/RvjeAVTeGa6Mt/yqno4Lxd1mNw/KlLF26nOUOjrj6b2R9cCCb95zgxKOmvkFz38DWbqCtPIv7cVtZ776GtSuWsWzpMpatXIOjmw/BkREE+UZyLCGXgnYbdkML+ufHidnoh6fHJnaevkuOTp1M92IuS+TKgQj83F1x8w/D+8gzylsN/RNpRYe24TkJ27wJ8XbD1W09W49cJrmwkKfxUWxVJ5arV7B8+UpWe24k4lY1jZ1menstdFY+5/HJMHyc1+Lk6om3fxBBgX74enrg5BXJkbullLWZRgzU7RjqX/D0eizb/J1Zs3Ipy5YuZdnyVaxx9cEneBMRQZ5Enn9OTrUOU+trylP3E6zm3cUFVzdvQqLPcjG3E1uvnbaceM5Gh+Dl5oqLmx9ugcdIKm2m+atCwhWs+haaKgvJTr/BpUNbCfNxYt06D3w3xXDw8gMyy1ox2Ie+ebdjs+jRNpRRknOXxHP7id7gi+e6dTh6bWT70esk51RQq/39a+M/1DlZaq6y9VjQOBgPCtauOhqvebJ2iTebzjwiT9/PlJoXRfuEazu98VwXSmRq8xAza7CtfCjPH/93GyZtDSVn3XFe6IDvvtvcrR3J0UAa/XuvdLW20Kk1YFTNqoGJoa2OrDPhhDq7snrfU7TvjAc7xppnZB91Z9IkHw4+LqdiYJ8Kmx57WxKxrr/hEXqMU886Bp9nbyP/yg62eKxj6fZ06ntsbyf9Cqb6HArivJk60YWYu68pHaLTu/wM5OszfirGWhpzj+IzZTJeBx5xv9z4th31LwHIPOCGr0cIwSeeorH3jmhjQzT4jLTez5+NXqWD/LPBhHoF4rc3jdeGD0RkfcXzbRYjel0LdQbzkGVJCuZuDcWp/kzfGo7bCOPB3pVJ3KUNzN+9Fe8XWvSq4aOmrRipeZPG/oMr+L/2XuZqdQvaPoNKwWpspS4jnLnbwnBKfszjgTru7cXencOF6+Es3BXBuqct6K2jm6RKTyVZ5yIJcVjJkvXXyO009bX/9zX7vZrL/aKpMCAMCAPCgDAgDAgDY8HAH8d4UEzUPzjKkU3+rNlwkTy9geKbEWzyCSJ4XyK5ncMHsEpPCy2vkjizYTVrVzrh6h/O1j0HOHw4ltioELwdFjB36o/MWBlI4IWyvomM0mtHX53F/dObCVyzjMXrgtmwYx/7Dx0kNnozm3xWsfCXSfw8YQ5eB1K5V2fFburC+OYut6LdWbtgFU6bzpLS0p8Xa0MuWamXOLQtCH/H35jgc5PcOm1/RIT6xr79DTnX4zgf5YHjwkUsWuFKcPRuNgQEsmlbDDGx+9m3aytbInexO60BTZelLxpCNR4yzmwheONO9h0/z8Xrt7h1I55zh7bgu/AXVm04z40X9cPMGHtnMU8v7ybSay3LVnkRvG0v+w4d5uDe7WwOcGbV3GlM+v4b5my8SXJhK6auBhqzr3B6qyNLpk3i16XeBB9J40mFuru+HV35E1KPrMd7xXymznHr+yyzTot26AT0iydHdizd7bRWvOBp2mXOxm4lMjQA/4BQQrfGcvRyGvfzqqjvUN+gDukgbEa0jaW8ykji5un97NwQQKCfP0HhO9lz4ioJjwt4remPBBnLt6bjZzzY6WmroGDvAn5bvpHdN19QPVRXaxnpR4IJcPLCI664bwnQWHQWvaoZVveYeI9pzF8czM5rOZT3DBoen5eGQnf1HS5u98bDbQPhiTX9EQN9LNjoKLlHSuRvTJgTxaWCepoHDEOlh15LPheDl+Hqs4s9ydWDxoP1DQ+OhRDk6I7LySJ0lsG33/aOEqqSN7Nowlw2XMont3l4P/B5eR5kSX1edWoki7+fS9jFPHLePU/VwcKbS+sJdfXBfVdS31KBMT3i0taNvfk+p4JW47V+HwfSa/s2+h1LZkfqoahL7jTPOH50NZP2HWZrdhl1A3XS24u14TbRp4KZvnc/O6uGRijYaavL4vo5J/7PTfs4VFZHfd99dnoMjWTfdGXSju34PcyjdCi7tkpSU6NYtSuQX5Ne02UeGtkyWA+qsVH38CRHfZcydVEoJwq7aBpiYIwsh/w9RLsv7nflXuFHGBAGhAFhQBgQBn4/A38c48FcS8bJTUQEhhF8oQh9r4Kp6hanNvngs34/J/O63oVzqzujG+uzyT4bzIJJc3CJPM2lJ2XUdBgxmw10NxXw6MwmglbOZPaKAPzOlvSZAYq9g5KEGHZ5rWSBYwSxiXm8bNChN/XQ3fqG4ntx7HWfzayJc/DYn8KdmoFBs4I5az9RHh44bzxD0rvJilqBVprzErgS4cAEz+tk13ViGjEwtBSeYq/vCmZPn8cy92CCDiTyoKSZVn0XWk0VbwoLKW620NN3fJ2CWddC85t8nhdWUtPYTHNLK60t9dQUPSAhdCbzFoWxJyGfyr6QaXXCZKMj+zh7AxxZuSaI8DMPeVHbRZfJQo+2jvLMy5yOWM3sH75hTuh1kvJb+yNATJ1YKq6w22URa333ciCtkq6BSYm9i8o7hzgQ4s2qkHieaW0Yhk4wRpTxyxuznZ7WMl49vNIXWeG/dgmz5q3GM/wIFx+XUtU1ygRTsWLpbqWx8B6Jp3YS4bUah6UrWO61lV3xzylqMYzZiS1qecbPeLBhaC7lUfhUFq7ZxoHkV4MTdFVXWwNZcRtZ7+zC6v3PMX/grfEXa26qR1N4mpAZE5jtcoizjyvRDzV4PlGn6nGLNl01ufERbPT0wmtrPKk1QzeXtKIpSOZS0By+W3aIxOImOgd4UpduWCtJ2LwSF8/NbL1aPGg8mPNI3u+Hz2pf/C/XYOjba6W/c1T05dQ93s2a7yfjfyKHzNqBNvl1naetuZDyy8HMmLCcLbdfUfjO0FTbkZWGxEgiXD1ZF3GFp+bR9ij4unTVPsvWrUFzJxy/Va6sj03iYYN1UINPaP/Fda0+TzGhay8i88l+5oavY+n1+9yu0w5bQmIuv0jk0WAmxR7jSLMN00B9qeZjYw73rnryb0FbiSqupvyt8dCtqyXt/Ap+2B1NWGbRW0PirS52DY/SY1gX7cP3V3LRmj9URoXu10kk7nJk+rSl+F6roaxt/PbV+Cr9xqNO5Jnjy7zoK/oKA8KAMCAMCAPjxsAfxHhQsDancibcCz//3Rx+2obBYMDQVcKdQ0Gs9wjA91QBOmUgFN9EQ/Z1zgYs4rsFO7n6Sl0CMHxCoOgLuXM4jLDASCKuVWBVN3o05nBrtxcea/zxP/MSvRoa/A5OBZu+ic6sPYSudSXi5H2F1Lc0AAAfHElEQVQeNQxMcj5uPDTm3OJ82LJPGA9LmbXAA6/DGdRYet/b1X9wYNy/N4RR20JLZR7PHqSSdPsWN28nk3jzOndiHJg/04OIS1kU9KhlVifoBnKOuOPj6IXvvlQKu0doYW5G8+oGMat+xW1XCveK2t+u1+4/uSDjgDueqz3wiUkkT9//ttne9YLEvYEEeIax6UZV3z4AY/1mVt1g02TU0V75nGeXInCc/gPTJs3CJfo2iWXqspPh5RjYrNNq6sagredVyiH2+ixkys8zmTRvCxcKG2gcun79vftHPu/jf4+n8aDXvOZe2EQWOO7gUFrx8L0L7E08O7uJUGdnVu7JHDPjwd75muo7W1j+wwQWbbhBQn7bu3X772s9Qhu7DYuukaasA0Q4/cbagAMcvV+DYZjGVprykrgQMJPvHI6RUqKha8DY6NszopbkrQ64eIQTefHlYP2ac0iM8cHbwY/ga/V0D4T7q8tODBXUZ8bg9P3P+Bx9zuOaD01kR+R3WL4GP7M15fMmPoApE1axLamIV11DIz5saFK2EenqjuOmeDJMY2g8KD0YNAUkrZ/JctdoDqWWDK/zD+T3k/XyofsUG2ZDNbk5Z/HdtYb/Hn2Ck2+aaBjRPkxl54g4HMTEAyc42Wobst+HHX1TLg+ve/Nv/hFsLaqirM94tGHQ1ZB4ZikTomPYmFVM40Adq3mxt/LkwV5c9njzzcVnaE0fri9bcxYZZwJZPHkmi6KfkVerH9IfD9bZV2vwIW3k3wfbnmghWggDwoAwIAwIA8LAZzLwxzAeFBvt93cR6eLA4lW+bDx8klOnTnHq5DH2hKxj3bJV/OZ3mkx9b/86YHsbpekniHZexKTgVArqu4aH5qviKRb0mmrqqqqpaFX3AVCwNyYQt8EFd6+dxGYZ3l+/rVixm9uoeV1KraYD7Tsz42PGg4X659c5HbSI7z4a8eCCo38sZ4u7+44f/OAkXjHQUnqfpL3erJw9ndlzFrF4+SpWrV7JisVzmDnxO76bsJaN8ZnkqQaDYqbXVkPi5hW4em1lx7VXGIZOBPq0sGM1dtH2Jp/S+k7auwcMFXVwb0eff5L9/mtY7bGTg1md2NT6eBLLLj9fvDafI63JMsYTgv4oDXVPiowre9nuvYJFv/zCtEWehB1M4H5xE+2jHWeoWDDr6ql4fIFjEa6sXTibWQtW4bjxKBcyamjQmUffqPIzG9PICc54Gg99EQ+b1YiHrX0RD+p+Au/St9WTGbeBEGdX1sRmj5nxYNPk8+aSH798NwGH3Q+4V/rxTVvf5UcNydfVUpNxgvDl0/nNdTeHU15Ra7CNaENqxEMKl9erEQ8HSSgaGvFgptda8TbiIZJtV0sGy2vJJ2W/H76rffC7VD084kFXTu2j3az+fgoBJ3PIHLYp5RDNPrOO1YiHiivr+WXCMiJvvXwv4qE+4W3Ew+YrPB/DiAeluxZNzjH8pk/Ha9897pYNbtY4VOcx+93SSM7TEwTu9+B/7NxNdEkzVd3W94wmc8VFthwNYlLsUY5ohkc8dDVmc1eNeAjeRlRJNRVDIh7uXFjBD7uiCc14NWzpRq+9iYfpe1i3x5cfrubS9cGIB9VUKiT3ZiSOE6cxdX0STys6xrT9jpmWn8mWpPfl7VE0E82EAWFAGBAGhIF/HAb+AMaDBcWqIWOfC75rVrJ0XQChmzez+e21ab0HLiuXsWRFILuydGjUyY69jdfpx9m1biETQ9J42agbdcCqWM19p3qY3i5hsDfc4lTYOtw8d3LgqXFw4jNsYGnH0mPCoh4v+W4CP2A8uI+y1MJEddZljvrM/7jx4OeGS/ARLld8+A2g2vDMdRk8OBWBu4MTLtvPcTH1MRnZueTm5vDs/m1SopYx/xdXNsVnDDEeqkmIWI6z1zZ2XC/COKw8b2FWj8c0GzGpJ1MMneSqUR9dxaQfCSFwnSeuMWnUGMtJ2u1KQMAOoq4UvBdN8vUdhB2zrom6gjvcOrGDSH9XXF088AqJYk9cAveyi3hd10Zn99DwfXXpgY7Wimwyrh9jf7gfrqtX4+ITRuSB81y5m03Bm3o0OjPmMTkdYLDxf5XxoB5baunp01k90nV0rdQ9Hip5FbuYpctC2Xkj5+2ymbdpW4q5eyiQACcffM6VjdnmkraGF5Se8WDSt9+zdn8mDytMH8jfoAZqlIm5vZTC1CNE+axlqWsUx1MKed3SPUrUjo3O1/e5s20ZE2ZtJT6/Ds3A8hx1jwdzDucDl+LmF83e1NrBtK2VPDoRRrCTG+uOFQ7f46GtiMqEcOZPWEj41ULyWkdZgtPHe//xsJaeHixDj4Ud0RbsWvXUjW0s/X42Iedf8LxpwIRT68pMyYUg1rv54hmdQql1aETUUE2+9HcbuuosMg658OvsQA7fL6VC/6FyfOmzR3xfsaKY6sl4fAS/wyHMO3qQbbllVKin2Yxo9yqb1qYkYuLWMyNmL9sqrBjffcdGS20ml8448p83H+Lomwaa+j5TMBkayUtwZ/KOrfg8yKV4oI5VrW1vSErZjsOuIOaklqOzDOg7Ip/qd80l5CdH4TZxKj/5XCejrG3Ufnz0NjTK80bUtdwnGgkDwoAwIAwIA8KAMDB2DPzjGw9WHbbWdI54OhC4IZo9F++Qnp4+eN1L4EJsBKGODiyNfs5LTTdmu4GarIuc8F3EDw4HSSppof1ddMJbcRUzek0VNRWVlGt6+iIe1NMCrm73wMMphA1X3vStbx9caqFGD6iD9hZqSkqoamyn491bdwXzswPs9HTBKfQE14euM7e38jr9BFFr542B8WCnPfs8Zza5MN8hkpP5zTQYBo5TtGFqe8ObY6tYOttt0HjotdKrtJOx3xlP5wCCj9znTffICa8Na4+W1rI8SmraadNbh7+pVrqpz4jjWJgrSxzWcyb5OIFrHAnec4Vbxdr33pJ+eQNWMLVV8PpZCjdP72PXJn+8PT3xD9vBnhNXuP24gJL6LnreLaVR69CG1dhOU9Fj7l87waEdGwn188HHfwPhe05xITGD7LImWg0jyjKGk48vMx4ULLomGgvTuHLyEEePn+Paw1eUNKvsjWzwClZdA62JATgvcSfsZDrP3+010Iu9NZ1L2zzxcNlE1IN2PmxgjHzux/+2Nb6g9JwXU779njX7nvCgfLS8DX2GgqX9NS8SDhET5ouzzw4OJr2krLWbHtVcMRrQqvoPMeh66rPJO6WeQuHK3vQyygxvWbR2YW+8RvS6pXhuOsW5F12DxoO9k1c3drHDy4klESlUGq1vTQ2l/5SMI+5MmezDwYdvqHiP7f69DPSaUl7eucCpwwc5fi6J+4W1aEbZOFOdlGsK4giaPgn3mLvcLR0I71eXHTXyYI8z3p4bCTubS/sYnWqhWDRUPDnLfucFzA+5zYPy9i/aW+Oz25vdhFFXQVbGMXwPh7Ds1DG2P3tJidqH2M3ou7sxmIdHBSn6bM5f3cTC3ZtxftZK18AyF0VHZWkiO/et4N8P3uJWbdvbPCvYetrQPN/Kwu3rWZ1wn3u6QRNFPSXj9OWNfadkeL3owNBn+g5lavB3xVRMXtIOXCZO5WffG2S8EePhs+v6vT5lUFd5hmghDAgDwoAwIAwIA+PBwEeNB0V9y22zYDEbacpLJS35HvdzK2g1W7FabdjVt7O/YwDT0dFBfX09LS0tg5OIL3meYsPaVUNLxg58lrkRefIemU3mIZEGvShWPfXPr3EhbCU/LYvhZkEDzSYLnW8ecm+vKzOmrCHi/EMelTbS3NmFXq9F29ZAdfFT7l08StzpK1zKbutfKmCr4dn5SDY6O7EiJI57JXXUtmrp0unoatfQWFlIwcOLHNtzmMsPXvF6yPpvy8tT7PdzZJXbdvY/rKVNZ0DX1UZzxSOSjobgPGc237qe52Fpc98Rcqr2fXsY9BjR5Rxlt7cL6wIOcL5Yh9Fo7Lt61HoY9lbchubxCY4GOzHPcTdXy1up69TRpW2jpb6MV4+vccbvV2ZNWUvIybtktvZg6nvLb6U2JYot7k44+sdwKrO8r1xanQ5dZyuamhIKMxK5uGczhxMKKax/P8Te2vycR6fDcZ01g5VOy5i5LIK9t/JGMTG+tCH3v0lufHqRczsD8HZxZp1XGJsPXiExu5Lazp7R33La9eiackmLDSXU3QlnN3/W7zjG2ZRcXreY3m7E+aV5+Yzv97UZM0ZzD9qKi4QfCeSXg0fYU6lHZzLRY7MP43OwUdvoqsziYawjs3/4M999N4WFwSc4k9U0avkUcyfWsnNsc3TAe/Np4l+0YbTZ6bUZaHpykF1+HriHHudW9dDIm8/I/0fan73lJRXXg5kzYQIrdtzlTsnApHuU56omXE8jJWkH2emzmhUOPgQfTiOnso7q+npqSrLJfZZFWm4jJvtgP2LvqqTuUSyu02bjf/ge6eVdmGxWLNpqapI24r3ChdBDqTx5F2mgpm2jJTuec5vdWLBmN9fLOmgx2bCZO6h9doXTgb8x1fEkicUaOt69kR+SZ1sL1c/iOeQ0jZ+++TPfTVlJ4NG7ZDSO8rZd0dFV/5jzbjNY7X+Ik/fe0GqyYbd0YahN5rDPSjxDD3Lk8ej1NljfQ9L/iOZqxIipKYsHpzfiON+JTcl1VHaMxyaKVrq7qsjPOYPHjhVM2LUFj9T7pNQ0UN1aT3VTIak5z8msbew7JvRdOWyNPHmwH699AUy++Jj8LhNGuw2TvpRHTw7hEOnEL0mvyGk3vltupfbJloYbBO/zZlHcOfaVtqFXGbB3U190lo3HQpl1JI64pqFLN97Xy96Vx/Or4ayaOIMZG+7wvFI9yvf9773Lq3z2df+fFd1EN2FAGBAGhAFhQBgYAwY+YjzYsPTo6WprRtPUQGlmAom3kkl5UkC5Rj0poR1djzrx/fqB3u81HmzGTlqL7pO6bRHzlwSw+UQCGRUNaPQDEwYr3W31VGVe5mqUMxO/W0zI2cdk1nah76qmNvMEYQsmMWeRO4G7z3L5ziOyMtO5f/M4ewIcWDFvLr95RxP7sLlvQKsep9lZeIPL29axcNYilgQe4MSt+zx48piHSRc4Ex2I89zJ/DLXk62Xn5HfMeRNnuYO8ZHOOMxbycrQE9x6nM2Thwlc2heC/4pf+PnnaXyzbDsn015R1d6N1WKgu62aksJ88m5sI8zRgRWum4lJeUFeXh55+QUUVTSi0ZqGmD8K+sJrXN7qxOxZK3GOuc6N9Cc8Sr/NteM7iPRYwuwpP/L9d4tw2XyU+GelVLb3L92wtzzh1h5fnBcuZqHzFg5ce0B6ZgaP714jPnYTQavmMnnSPDyOZpJVpR6ZOaLe7VqqM+M54jSJv/z5B+aH3yblZduwHfC/bgKgaqin4NJOYqN2svvcPTIrtX1vtD9qelmaaS1LItbbl+2xl0nMqaLxHRcj8j6yLF/9t4LVpKO9tZwXlUVkZh3ELcaTH6OjCHr2ksyKEgpaOmntGS3KwkpTfjLxfpP54ds/85e/fMOPq7YRdaP4vVNO+nTs25ujirTdLni7BBB0MIW8ukba6p5zNdIJV9eNbDr9lHpb7xA+fl+5Fd0bah/uxvGnCSxYf5mbL5pHNUXU/CmWLqzVV9njNp85Mxay2GUDu48c4+ixYxw7dpTYCC98vf1YsfMhberynQHNFT1dDZlc8vmVFS5biLn6lOK6aqryEjnuPpOlbjEcv/ealhH9jr0tl2cXt+A2fxnOB56Q8bqeutJ0Uo6F4rx4Je5nXlPa3PM+t2q61nIK02IImPwt3/3lz/zlm4k4RF7iapF6PO1IzRQsXY1UXfLFY6UL/nuukFxUR1N1PtknPHFZ5kbYsbtkvj0y9/37Rz7vE38rJhofx3EyxJlZTodJbzH9ziNpP5CevZOK0ttsj17Mf/JexsQjMXjfOEtU0jmiEk6y4+oGpoRvxDstg0zz0GfYaXmTxInzQfy/WyKJyK8kv6WBlwWX2H0qgD9v20lMlYH6d9Ff6r1Wem213L4eytK9m1h67S4ZLS00teRzOj6IBXu3sibpGZX2j2/OaW14zKPjvsyfPIflB15QUN89ev2+V4dD8y+//25GRd9R+gnhSrgSBoQBYUAYEAZGMvBh48HaSmPpU9KvxBMfH8/5s6c5ffoMZ86e50L8RS5evE76qxY0uoFJ/peL+/uMBxsthancDF/AtB+/5dtvv+eHidNY5B1FdFpj34RIsVaReSaS0N+mMOnH7/jzn7/l+8nLCDz5kCf1JszaWuoeHyXKfQmLfpnK5ClTmDp1ClMmT2L6vNW4bznD5YwKmnsGy2g3d9KQn8y1KFcWTZ/INPX76jVlKtN+Xcgil0hiEwsoajJgHjo5srbzOjmWaLe5TPrxZyZPncrUqTNZ6LQeL18/gpb/wP/89kemux7hUlYlTVXPyD7hxdxp05g+6Ud++O5bvp3wAz9Nnsa0aeo1ndnue4hNek33kIGf0l1Ncdphtq+ewY8/TWLylKlMmTKdWYvX4RYazc2L4ayZM50pP05hgccOou9q+k0Vew+dbx6QciQE1/nTmDhJLZd672Sm/jKPBY6hbD6bRX591wfCn23oyx7wcM9avv/JkV1ppbzWjtw88MsZ6QdWwdKtQ69GYHSb+jZL/KjpoOqh2LBbe9B3dqLTd6NGhwzuufG1+fjUfSZqK9I5dnwV/3X9Ev49YC7/6vUr/+I1m/8csJh/D/6N/+fwJU6W1I8SKm/H2FhAXnwIq375kcnTFuEcdZWEV9oPTKbUSBAr+jcpXIryYO38X/ll7iKWzJnOjPmuhB6/S0atcXBCP4SRkZ3AZ/9t1dBSeoUts79n5toYTt5/g/bdMonh2ti7W9E/2UHAsun89M03fPvdD/z088/8/Pb66YfvmbTIA6dDuXRZ7EPKqGDvaUdbGM8+32UsmzeHWXPns3DubKbM9yM64SVFreYh33+brt1IR/kT7h3w4LdpM5i3YCFz58xi7uJ1OO+4TU6rCf2HwvYVHU0vk7kUuoSZP//ItIUBbL/8jJdDlq8M1Uixm7BqX5K23w+vZfP4ddZcFi+ay/Qp83HdfZu0opYhex0M12Xocz7rd1MJD4+HEuTkieuJV7SpkRxjUZcjn2GpIS/3BGtCfuVf3GfwL95z+Fe/efzbwOU7i/8jKAyv+9kUjDjZwm7WUFp0nS2xa/iPkFX8z4hV/N9hq/kmOgrvjDc0mGzvb97ba0PX+JC4Kxv4ZdMy/stGJ77d+Bv/sSmQVbfukaIxftDU6tdNQf/yJje3rmLK9JWEJGuo6Bjspz9L25EayN8ygRYGhAFhQBgQBoQBYWDcGPiw8aCY6NG101xfS23tyKuOurp6mrt+X8j67zMeFExaDQ3FT8l48oQnfVcGz/JLKevbk0GdfBpoqynh1fOMt5+r38vmVVULrUYFxWbG2lVP5cssHiVf4/LZOE6cPMe5K0ncyXhBQXljXxmHD/TVY+baaKnIJ/tBAjfi44iLO83pize4dTeT7FcV1LZ3020ZjHboHwRb6W6t5k3OPZIvn+H40eOcvpzMnayXFBaXUJqf2ZfHzPxK6toN9Bja6ajKJ/Nd2QbKOPgzM6+M8ibd8ImI3YShTU3nLokX1ZM9TnH64i0SH+RQUFpFa3M5hTnPyHryVqshewjYejpprysmPyONhIuniVPvPXeVGykPycwv7UvLYLG9P+lTG6i9g+qsCxz3W84M38vcf9NO19BN4/4pGrGdbkMzlZU53Hn5jLRRrnsVdVTpRp9U2U06dE2lFDzP4umzF7yq1NDyiT0olJ52NG9yybpzg0tnzxB39goJD17wqrYd7bC3zL9zAqzWn9KDvimPlNBZLFrgzZb4TIoG9mAYUb99bautjKIXzz7IcGbOSwqrtVhHLtmyW7F3a6h++YT7CVeIP3uOcxdvk5JVTHmLoe+4zPcnlkrfPiQd1QU8v3ONqxfOcvbCNW7efU5eZSt6mzI6t335tmHWt6IpyyM7K4tnL15T0aRFP2KCPZimnV57N+01L3nxIIGb8Wc5c+4iV1IyyS1voe0TdTb4nE/Xia3qNue2rcc7YD/ni/Vvl0Z9+r4vSaPvu/ZutNoa8kqef5DdtOLXvGrTDh5xOlDnigWjQUN5xVNuPL7GoTuXOfT4HtdLyijR9mBRRu4Z05//PhO3sZAHuSkcu3OZvXducT4vn+eadtoto9/zrlyKjvK0Q+x1W8L05du4Uq6ndax5Hyif/By3Aci7+hSNRWNhQBgQBoQBYeAPz8CHjYe/QuX/PuNhDAffihWTvoN2TSP1Dc00d3TTYx36FnaUtNQ9GCx6tC2NNDY20aQOyEcNoR96r4LN3I2hvYn62jqa2g10m8cqKmBoOnbsViOGtkYa6xtoautC1/O56dixW9R7m2isb6SpuQOtwYz1A2+2+weOCpbmHB6f24bvag/W366mqnOUt9J/Bab+KQey6jGhhk7aGhuoa+zoXwL10foaysqX/K5g0TdSn7IRv6XL8dp5hduloxwrO1b1bDdjVPcnaWyisaULo2oefKpcaqRLTydtmkYamzvoMIz1Ua5D9bJjMWr7+oCGxhbajSP3XBn63a/73d6Sz/N7d0i4k09l92eUf6y0/+LnqHs0WDDqW6hv09Cg06OzjjRfR9FA7XtNXTS3NVHV1kGn2TpKdMT79ym6V9w/Hoa/gxNrdqbzWmfp21z2n7L9f3Fdva+n6CaaCAPCgDAgDAgDwsB4MyDGgwzavs5dsxnp0dbz5mUBLwvyeXpjPzGBjsxdEsSee2W8buuh558u4uGfoMOyGrC3POTchrV4BOxhz+0ytGN0esN4d3b/mM+3YbVasbwXQfVPwNoH+2YbXa8ucCLCG2fPnRzOauuLMPvk8qsPPu+fWUsp+z9mvyD1JvUmDAgDwoAw8I/HgBgPMhj9KuNB6a6jKe8Su3w98HV3w3H5AhbOnsmM+Y44++/jan4TDcZPhEuL9l+l/d+2o1Xr1EzdnRh2hWwidF8Shcbejyxj+MfrFP+2+opeH9dfjaIwUHwpgm0h4YQdf0KV5eObUH78eaK36CMMCAPCgDAgDAgDwsBfgwExHmTy+3WTX4sWXUMe9y5f4PKF85w7e4YzZ9TNRy9x4eojChp1dH1wjbw07r9G4x6/NBRs3bW8vJdMasIDstvtn14CIe3s69qZ6DZCNxu99lYKkq+SdjeLnAbjJ5aBSV8zfv2AaCvaCgPCgDAgDAgDwsDnMyDGgwzsRwzsPxMexYpNPTpS04SmSd3n4u3VpKFJ04HeZJMJwR+ZLcWMoaOVtpY2Os3KmB3ZKZ33Z7a/PzJbHy2bQq9ipqtFQ1t7F/pPbUL50WeJ1tLehAFhQBgQBoQBYUAY+GsxIMaDDEy/zngQ3UQ3YUAYEAaEAWFAGBAGhAFhQBgQBoSBz2BAjIfPEOmv5QJJOuI4CgPCgDAgDAgDwoAwIAwIA8KAMCAM/NEYEONBjAdx6IQBYUAYEAaEAWFAGBAGhAFhQBgQBoSBcWNAjAeBa9zg+qO5dFIecZ6FAWFAGBAGhAFhQBgQBoQBYUAY+HIGxHgQ40GMB2FAGBAGhAFhQBgQBoQBYUAYEAaEAWFg3BgQ40HgGje4xAn8cidQNBPNhAFhQBgQBoQBYUAYEAaEAWHgj8bA39R40Ov1tLW1odVqZfIrBogwIAwIA8KAMCAMCAPCgDAgDAgDwoAw8Adk4G9qPPzRXBwpjziTwoAwIAwIA8KAMCAMCAPCgDAgDAgDwsBwBsR4+AO6SQL5cMhFD9FDGBAGhAFhQBgQBoQBYUAYEAaEgb8dA2I8iPEgoUzCgDAgDAgDwoAwIAwIA8KAMCAMCAPCwLgxIMaDwDVucImj+LdzFEV70V4YEAaEAWFAGBAGhAFhQBgQBv5eGBDjQYwHMR6EAWFAGBAGhAFhQBgQBoQBYUAYEAaEgXFjQIwHgWvc4Pp7cdckH+L0CgPCgDAgDAgDwoAwIAwIA8KAMPC3Y0CMBzEexHgQBoQBYUAYEAaEAWFAGBAGhAFhQBgQBsaNATEeBK5xg0scxb+doyjai/bCgDAgDAgDwoAwIAwIA8KAMPD3woAYD2I8iPEgDAgDwoAwIAwIA8KAMCAMCAPCgDAgDIwbA3+y2mzIJRoIA8KAMCAMCAPCgDAgDAgDwoAwIAwIA8LAeDDwJ53BiFyigTAgDAgDwoAwIAwIA8KAMCAMCAPCgDAgDIwHA3/SG03IJRoIA8KAMCAMCAPCgDAgDAgDwoAwIAwIA8LAeDDwp//v//9fyCUaCAPCgDAgDAgDwoAwIAwIA8KAMCAMCAPCwHgw8CfkP1FAFBAFRAFRQBQQBUQBUUAUEAVEAVFAFBAFxkkBMR7GSVh5rCggCogCooAoIAqIAqKAKCAKiAKigCggCoAYD0KBKCAKiAKigCggCogCooAoIAqIAqKAKCAKjJsCYjyMm7TyYFFAFBAFRAFRQBQQBUQBUUAUEAVEAVFAFBDjQRgQBUQBUUAUEAVEAVFAFBAFRAFRQBQQBUSBcVNAjIdxk1YeLAqIAqKAKCAKiAKigCggCogCooAoIAqIAmI8CAOigCggCogCooAoIAqIAqKAKCAKiAKigCgwbgqI8TBu0sqDRQFRQBQQBUQBUUAUEAVEAVFAFBAFRAFR4H8DVLBSNpW0V2EAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题九⼗六：神经⽹络（Neural Network）第⼆步——训练\n",
    "![image.png](attachment:f0818dd2-f9c9-48f4-890b-eb466f645973.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get HOG\n",
    "def HOG(img):\n",
    "    # Grayscale\n",
    "    def BGR2GRAY(img):\n",
    "        gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]\n",
    "        return gray\n",
    "\n",
    "    # Magnitude and gradient\n",
    "    def get_gradXY(gray):\n",
    "        H, W = gray.shape\n",
    "\n",
    "        # padding before grad\n",
    "        gray = np.pad(gray, (1, 1), 'edge')\n",
    "\n",
    "        # get grad x\n",
    "        gx = gray[1:H+1, 2:] - gray[1:H+1, :W]\n",
    "        # get grad y\n",
    "        gy = gray[2:, 1:W+1] - gray[:H, 1:W+1]\n",
    "        # replace 0 with \n",
    "        gx[gx == 0] = 1e-6\n",
    "\n",
    "        return gx, gy\n",
    "\n",
    "    # get magnitude and gradient\n",
    "    def get_MagGrad(gx, gy):\n",
    "        # get gradient maginitude\n",
    "        magnitude = np.sqrt(gx ** 2 + gy ** 2)\n",
    "\n",
    "        # get gradient angle\n",
    "        gradient = np.arctan(gy / gx)\n",
    "\n",
    "        gradient[gradient < 0] = np.pi / 2 + gradient[gradient < 0] + np.pi / 2\n",
    "\n",
    "        return magnitude, gradient\n",
    "\n",
    "    # Gradient histogram\n",
    "    def quantization(gradient):\n",
    "        # prepare quantization table\n",
    "        gradient_quantized = np.zeros_like(gradient, dtype=np.int)\n",
    "\n",
    "        # quantization base\n",
    "        d = np.pi / 9\n",
    "\n",
    "        # quantization\n",
    "        for i in range(9):\n",
    "            gradient_quantized[np.where((gradient >= d * i) & (gradient <= d * (i + 1)))] = i\n",
    "\n",
    "        return gradient_quantized\n",
    "\n",
    "\n",
    "    # get gradient histogram\n",
    "    def gradient_histogram(gradient_quantized, magnitude, N=8):\n",
    "        # get shape\n",
    "        H, W = magnitude.shape\n",
    "\n",
    "        # get cell num\n",
    "        cell_N_H = H // N\n",
    "        cell_N_W = W // N\n",
    "        histogram = np.zeros((cell_N_H, cell_N_W, 9), dtype=np.float32)\n",
    "\n",
    "        # each pixel\n",
    "        for y in range(cell_N_H):\n",
    "            for x in range(cell_N_W):\n",
    "                for j in range(N):\n",
    "                    for i in range(N):\n",
    "                        histogram[y, x, gradient_quantized[y * 4 + j, x * 4 + i]] += magnitude[y * 4 + j, x * 4 + i]\n",
    "\n",
    "        return histogram\n",
    "\n",
    "        # histogram normalization\n",
    "    def normalization(histogram, C=3, epsilon=1):\n",
    "        cell_N_H, cell_N_W, _ = histogram.shape\n",
    "        ## each histogram\n",
    "        for y in range(cell_N_H):\n",
    "            for x in range(cell_N_W):\n",
    "            #for i in range(9):\n",
    "                histogram[y, x] /= np.sqrt(np.sum(histogram[max(y - 1, 0) : min(y + 2, cell_N_H),\n",
    "                                                            max(x - 1, 0) : min(x + 2, cell_N_W)] ** 2) + epsilon)\n",
    "\n",
    "        return histogram\n",
    "\n",
    "    # 1. BGR -> Gray\n",
    "    gray = BGR2GRAY(img)\n",
    "\n",
    "    # 1. Gray -> Gradient x and y\n",
    "    gx, gy = get_gradXY(gray)\n",
    "\n",
    "    # 2. get gradient magnitude and angle\n",
    "    magnitude, gradient = get_MagGrad(gx, gy)\n",
    "\n",
    "    # 3. Quantization\n",
    "    gradient_quantized = quantization(gradient)\n",
    "\n",
    "    # 4. Gradient histogram\n",
    "    histogram = gradient_histogram(gradient_quantized, magnitude)\n",
    "    \n",
    "    # 5. Histogram normalization\n",
    "    histogram = normalization(histogram)\n",
    "\n",
    "    return histogram\n",
    "\n",
    "\n",
    "# get IoU overlap ratio\n",
    "def iou(a, b):\n",
    "    # get area of a\n",
    "    area_a = (a[2] - a[0]) * (a[3] - a[1])\n",
    "    # get area of b\n",
    "    area_b = (b[2] - b[0]) * (b[3] - b[1])\n",
    "\n",
    "    # get left top x of IoU\n",
    "    iou_x1 = np.maximum(a[0], b[0])\n",
    "    # get left top y of IoU\n",
    "    iou_y1 = np.maximum(a[1], b[1])\n",
    "    # get right bottom of IoU\n",
    "    iou_x2 = np.minimum(a[2], b[2])\n",
    "    # get right bottom of IoU\n",
    "    iou_y2 = np.minimum(a[3], b[3])\n",
    "\n",
    "    # get width of IoU\n",
    "    iou_w = iou_x2 - iou_x1\n",
    "    # get height of IoU\n",
    "    iou_h = iou_y2 - iou_y1\n",
    "\n",
    "    # get area of IoU\n",
    "    area_iou = iou_w * iou_h\n",
    "    # get overlap ratio between IoU and all area\n",
    "    iou = area_iou / (area_a + area_b - area_iou)\n",
    "\n",
    "    return iou\n",
    "\n",
    "# resize using bi-linear\n",
    "def resize(img, h, w):\n",
    "    # get shape\n",
    "    _h, _w, _c  = img.shape\n",
    "\n",
    "    # get resize ratio\n",
    "    ah = 1. * h / _h\n",
    "    aw = 1. * w / _w\n",
    "\n",
    "    # get index of each y\n",
    "    y = np.arange(h).repeat(w).reshape(w, -1)\n",
    "    # get index of each x\n",
    "    x = np.tile(np.arange(w), (h, 1))\n",
    "\n",
    "    # get coordinate toward x and y of resized image\n",
    "    y = (y / ah)\n",
    "    x = (x / aw)\n",
    "\n",
    "    # transfer to int\n",
    "    ix = np.floor(x).astype(np.int32)\n",
    "    iy = np.floor(y).astype(np.int32)\n",
    "\n",
    "    # clip index\n",
    "    ix = np.minimum(ix, _w-2)\n",
    "    iy = np.minimum(iy, _h-2)\n",
    "\n",
    "    # get distance between original image index and resized image index\n",
    "    dx = x - ix\n",
    "    dy = y - iy\n",
    "\n",
    "    dx = np.tile(dx, [_c, 1, 1]).transpose(1, 2, 0)\n",
    "    dy = np.tile(dy, [_c, 1, 1]).transpose(1, 2, 0)\n",
    "    \n",
    "    # resize\n",
    "    out = (1 - dx) * (1 - dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix + 1] + (1 - dx) * dy * img[iy + 1, ix] + dx * dy * img[iy + 1, ix + 1]\n",
    "    out[out > 255] = 255\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "# neural network\n",
    "class NN:\n",
    "    def __init__(self, ind=2, w=64, w2=64, outd=1, lr=0.1):\n",
    "        # layer 1 weight\n",
    "        self.w1 = np.random.normal(0, 1, [ind, w])\n",
    "        # layer 1 bias\n",
    "        self.b1 = np.random.normal(0, 1, [w])\n",
    "        # layer 2 weight\n",
    "        self.w2 = np.random.normal(0, 1, [w, w2])\n",
    "        # layer 2 bias\n",
    "        self.b2 = np.random.normal(0, 1, [w2])\n",
    "        # output layer weight\n",
    "        self.wout = np.random.normal(0, 1, [w2, outd])\n",
    "        # output layer bias\n",
    "        self.bout = np.random.normal(0, 1, [outd])\n",
    "        # learning rate\n",
    "        self.lr = lr\n",
    "    def see(self):\n",
    "        return self\n",
    "\n",
    "    def forward(self, x):\n",
    "        # input tensor\n",
    "        self.z1 = x\n",
    "        # layer 1 output tensor\n",
    "        self.z2 = sigmoid(np.dot(self.z1, self.w1) + self.b1)\n",
    "        # layer 2 output tensor\n",
    "        self.z3 = sigmoid(np.dot(self.z2, self.w2) + self.b2)\n",
    "        # output layer tensor\n",
    "        self.out = sigmoid(np.dot(self.z3, self.wout) + self.bout)\n",
    "        return self.out\n",
    "\n",
    "    def train(self, x, t):\n",
    "        # backpropagation output layer\n",
    "        #En = t * np.log(self.out) + (1-t) * np.log(1-self.out)\n",
    "        En = (self.out - t) * self.out * (1 - self.out)\n",
    "        # get gradients for weight and bias\n",
    "        grad_wout = np.dot(self.z3.T, En)\n",
    "        grad_bout = np.dot(np.ones([En.shape[0]]), En)\n",
    "        # update weight and bias\n",
    "        self.wout -= self.lr * grad_wout\n",
    "        self.bout -= self.lr * grad_bout\n",
    "\n",
    "        # backpropagation inter layer\n",
    "        # get gradients for weight and bias\n",
    "        grad_u2 = np.dot(En, self.wout.T) * self.z3 * (1 - self.z3)\n",
    "        grad_w2 = np.dot(self.z2.T, grad_u2)\n",
    "        grad_b2 = np.dot(np.ones([grad_u2.shape[0]]), grad_u2)\n",
    "        # update weight and bias\n",
    "        self.w2 -= self.lr * grad_w2\n",
    "        self.b2 -= self.lr * grad_b2\n",
    "        \n",
    "        # get gradients for weight and bias\n",
    "        grad_u1 = np.dot(grad_u2, self.w2.T) * self.z2 * (1 - self.z2)\n",
    "        grad_w1 = np.dot(self.z1.T, grad_u1)\n",
    "        grad_b1 = np.dot(np.ones([grad_u1.shape[0]]), grad_u1)\n",
    "        # update weight and bias\n",
    "        self.w1 -= self.lr * grad_w1\n",
    "        self.b1 -= self.lr * grad_b1\n",
    "\n",
    "# sigmoid\n",
    "def sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    "\n",
    "# train\n",
    "def train_nn(nn, train_x, train_t, iteration_N=10000):\n",
    "    # each iteration\n",
    "    for i in range(iteration_N):\n",
    "        # feed-forward data\n",
    "        nn.forward(train_x)\n",
    "        # update parameter\n",
    "        nn.train(train_x, train_t)\n",
    "\n",
    "    return nn\n",
    "\n",
    "# test\n",
    "def test_nn(nn, test_x, test_t, pred_th=0.5):\n",
    "    accuracy_N = 0.\n",
    "\n",
    "    # each data\n",
    "    for data, t in zip(test_x, test_t):\n",
    "        # get prediction\n",
    "        prob = nn.forward(data)\n",
    "\n",
    "        # count accuracy\n",
    "        pred = 1 if prob >= pred_th else 0\n",
    "        if t == pred:\n",
    "            accuracy_N += 1\n",
    "\n",
    "    # get accuracy \n",
    "    accuracy = accuracy_N / len(db)\n",
    "\n",
    "    print(\"Accuracy >> {} ({} / {})\".format(accuracy, accuracy_N, len(db)))\n",
    "\n",
    "\n",
    "# crop bounding box and make dataset\n",
    "def make_dataset(img, gt, Crop_N=200, L=60, th=0.5, H_size=32):\n",
    "    # get shape\n",
    "    H, W, _ = img.shape\n",
    "\n",
    "    # get HOG feature dimension\n",
    "#     HOG_feature_N = ((H_size // 8) ** 2) * 9\n",
    "\n",
    "    # prepare database\n",
    "    db = np.zeros([Crop_N, 10800 + 1])\n",
    "\n",
    "    # each crop\n",
    "    for i in range(Crop_N):\n",
    "        # get left top x of crop bounding box\n",
    "        x1 = np.random.randint(W - L)\n",
    "        # get left top y of crop bounding box\n",
    "        y1 = np.random.randint(H - L)\n",
    "        # get right bottom x of crop bounding box\n",
    "        x2 = x1 + L\n",
    "        # get right bottom y of crop bounding box\n",
    "        y2 = y1 + L\n",
    "\n",
    "        # get bounding box\n",
    "        crop = np.array((x1, y1, x2, y2))\n",
    "\n",
    "        _iou = np.zeros((3,))\n",
    "        _iou[0] = iou(gt, crop)\n",
    "        #_iou[1] = iou(gt2, crop)\n",
    "        #_iou[2] = iou(gt3, crop)\n",
    "\n",
    "        # get label\n",
    "        if _iou.max() >= th:\n",
    "            cv2.rectangle(img, (x1, y1), (x2, y2), (0,0,255), 1)\n",
    "            label = 1\n",
    "        else:\n",
    "            cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 1)\n",
    "            label = 0\n",
    "\n",
    "        # crop area\n",
    "        crop_area = img[y1:y2, x1:x2]\n",
    "#         print(\"crop_area\",crop_area.shape)\n",
    "        # resize crop area\n",
    "#         crop_area = resize(crop_area, H_size, H_size)\n",
    "#         print(\"crop_area resize\",crop_area.shape)\n",
    "        # get HOG feature\n",
    "#         _hog = HOG(crop_area)\n",
    "#         print(\"_hog\",_hog.shape)\n",
    "        # store HOG feature and label\n",
    "        db[i, :10800] = crop_area.ravel()\n",
    "        db[i, -1] = label\n",
    "\n",
    "    return db\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r\"D:\\work space\\git\\ImageProcessing100Wen\\Question_91_100\"\n",
    "img_path = os.path.join(path, 'imori.jpg')\n",
    "np.random.seed(0)\n",
    "# img_path\n",
    "def plt_img(img):\n",
    "    cv2.imwrite(\"out.jpg\", img)\n",
    "    img = Image.open(\"out.jpg\")\n",
    "    img = np.array(img)\n",
    "    plt.imshow(img)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((128, 128, 3), (16, 16, 9), (200, 145))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape,histogram.shape,db.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 4, 16, 10800)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((4) ** 2) * 9,32//8,4**2,60*60*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-32-c5abf6fb6f7f>:232: RuntimeWarning: overflow encountered in exp\n",
      "  return 1. / (1. + np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy >> 0.865 (173.0 / 200)\n"
     ]
    }
   ],
   "source": [
    "# Read image\n",
    "img = cv2.imread(img_path).astype(np.float32)\n",
    "\n",
    "# # get HOG\n",
    "# histogram = HOG(img)\n",
    "# prepare gt bounding box\n",
    "gt = np.array((47, 41, 129, 103), dtype=np.float32)\n",
    "\n",
    "# get database\n",
    "# 生成200个数据\n",
    "db = make_dataset(img, gt)\n",
    "\n",
    "# train neural network\n",
    "# get input feature dimension\n",
    "input_dim = db.shape[1] - 1\n",
    "# prepare train data X\n",
    "train_x = db[:, :10800]\n",
    "# prepare train data t\n",
    "train_t = db[:, -1][..., None]\n",
    "\n",
    "# prepare neural network\n",
    "nn = NN(ind=input_dim, lr=0.01)\n",
    "# training\n",
    "nn = train_nn(nn, train_x, train_t, iteration_N=2)\n",
    "\n",
    "# test\n",
    "test_nn(nn, train_x, train_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9216,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 作者：thothsun\n",
    "# 链接：https://www.zhihu.com/question/20467170/answer/839255695\n",
    "# 来源：知乎\n",
    "# 著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。\n",
    "# 缩放过程可以分为以下几种：缩放到均值为0，方差为1（Standardization——StandardScaler()）\n",
    "# 缩放到0和1之间（Standardization——MinMaxScaler()）\n",
    "# 缩放到-1和1之间（Standardization——MaxAbsScaler()）\n",
    "# 缩放到0和1之间，保留原始数据的分布（Normalization——Normalizer()）\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def plot(data, title):\n",
    "    sns.set_style('dark')\n",
    "    f, ax = plt.subplots()\n",
    "    ax.set(ylabel='frequency')\n",
    "    ax.set(xlabel='height(blue) / weight(green)')\n",
    "    ax.set(title=title)\n",
    "    sns.distplot(data[:, 0:1], color='blue')\n",
    "    sns.distplot(data[:, 1:2], color='green')\n",
    "#     plt.savefig(title + '.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cedar\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cedar\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cedar\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cedar\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "height = np.random.normal(loc=168, scale=5, size=20).reshape(-1, 1)\n",
    "weight = np.random.normal(loc=70, scale=10, size=20).reshape(-1, 1)\n",
    "\n",
    "original_data = np.concatenate((height, weight), axis=1)\n",
    "plot(original_data, 'Original')\n",
    "\n",
    "normalizer_data = preprocessing.Normalizer().fit_transform(original_data)\n",
    "plot(normalizer_data, 'Normalizer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89565378, 0.44475196],\n",
       "       [0.92690431, 0.37529775],\n",
       "       [0.92436356, 0.38151279],\n",
       "       [0.95312164, 0.3025874 ],\n",
       "       [0.93261116, 0.36088284],\n",
       "       [0.91991998, 0.39210614],\n",
       "       [0.94891279, 0.31553844],\n",
       "       [0.91892711, 0.39442739],\n",
       "       [0.9328143 , 0.36035744],\n",
       "       [0.93071935, 0.36573417],\n",
       "       [0.93285664, 0.3602478 ],\n",
       "       [0.88198854, 0.47127085],\n",
       "       [0.9243115 , 0.38163891],\n",
       "       [0.93630918, 0.35117676],\n",
       "       [0.89769744, 0.44061242],\n",
       "       [0.94390188, 0.33022604],\n",
       "       [0.91449202, 0.40460393],\n",
       "       [0.95855144, 0.28491952],\n",
       "       [0.94474306, 0.32781175],\n",
       "       [0.91288206, 0.40822341]])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalizer_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normalizer()"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.Normalizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = np.array([-100,-50,0,50,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    "def bn(x):\n",
    "    u = np.sum(x)/len(x)\n",
    "    q = np.sum((x - u)**2)/len(x)\n",
    "    xi = (x -u)/(q+0.00001)**0.5\n",
    "    return xi\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-21.0, 21.0, -0.05, 1.05)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-20,20,0.1)\n",
    "y = sigmoid(x)\n",
    "# plt.scatter(x, y, s=1) \n",
    "plt.plot(x,y)\n",
    "plt.axis([-21, 21, -0.05, 1.05]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-21.0, 21.0, -0.05, 1.05)"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-20,-10,0.1)\n",
    "plt.scatter(bn(x), sigmoid(bn(x)), s=1,color=\"red\") \n",
    "plt.axis([-21, 21, -0.05, 1.05]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-21.0, 21.0, -0.05, 1.05)"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, sigmoid(x), s=1,color=\"red\") \n",
    "plt.axis([-21, 21, -0.05, 1.05]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
